{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding Expected SARSA: A Complete Guide\n",
    "\n",
    "# Table of Contents\n",
    "\n",
    "- [Introduction](#introduction)\n",
    "- [What is Expected SARSA?](#what-is-expected-sarsa)\n",
    "- [Where and How Expected SARSA is Used](#where-and-how-expected-sarsa-is-used)\n",
    "- [Mathematical Foundation of Expected SARSA](#mathematical-foundation-of-expected-sarsa)\n",
    "  - [Complex Original Version](#complex-original-version)\n",
    "  - [Simplified Version](#simplified-version)\n",
    "- [Step-by-Step Explanation of Expected SARSA](#step-by-step-explanation-of-expected-sarsa)\n",
    "- [Key Components of Expected SARSA](#key-components-of-expected-sarsa)\n",
    "  - [Q-Table](#q-table)\n",
    "  - [Exploration vs. Exploitation (Policy)](#exploration-vs-exploitation-policy)\n",
    "  - [Learning Rate (α)](#learning-rate-a)\n",
    "  - [Discount Factor (γ)](#discount-factor-g)\n",
    "- [Expected SARSA vs. SARSA vs. Q-Learning](#expected-sarsa-vs-sarsa-vs-q-learning)\n",
    "- [Practical Example: Grid World](#practical-example-grid-world)\n",
    "- [Setting up the Environment](#setting-up-the-environment)\n",
    "- [Creating a Simple Environment](#creating-a-simple-environment)\n",
    "- [Implementing the Expected SARSA Algorithm](#implementing-the-expected-sarsa-algorithm)\n",
    "- [Q-Table Initialization and Updates](#q-table-initialization-and-updates)\n",
    "- [Exploration vs. Exploitation Strategy](#exploration-vs-exploitation-strategy)\n",
    "- [Running the Expected SARSA Algorithm](#running-the-expected-sarsa-algorithm)\n",
    "- [Visualizing the Learning Process](#visualizing-the-learning-process)\n",
    "- [Analyzing Q-Values and Optimal Policy](#analyzing-q-values-and-optimal-policy)\n",
    "- [Testing with Different Hyperparameters (Optional)](#testing-with-different-hyperparameters-optional)\n",
    "- [Applying Expected SARSA to Different Environments (Cliff Walking)](#applying-expected-sarsa-to-different-environments-cliff-walking)\n",
    "- [Common Challenges and Solutions](#common-challenges-and-solutions)\n",
    "- [Expected SARSA vs. Other Reinforcement Learning Algorithms](#expected-sarsa-vs-other-reinforcement-learning-algorithms)\n",
    "  - [Advantages of Expected SARSA](#advantages-of-expected-sarsa)\n",
    "  - [Limitations of Expected SARSA](#limitations-of-expected-sarsa)\n",
    "  - [Related Algorithms](#related-algorithms)\n",
    "- [Conclusion](#conclusion)\n",
    "\n",
    "## What is Expected SARSA?\n",
    "\n",
    "Expected SARSA is an **on-policy** temporal difference (TD) control algorithm in reinforcement learning. It is a refinement of the standard SARSA algorithm. While SARSA uses the Q-value of the *specific* next state-action pair ($Q(s', a')$) chosen by the policy to update the current Q-value, Expected SARSA uses the *expected* Q-value over *all possible* next actions, weighted by their probabilities according to the current policy.\n",
    "\n",
    "This use of an expected value generally leads to lower variance in the updates compared to SARSA, as it doesn't rely on a single sampled next action, which might be exploratory or suboptimal. This can result in faster and more stable learning in some environments.\n",
    "\n",
    "## Where and How Expected SARSA is Used\n",
    "\n",
    "Expected SARSA shares many applications with SARSA and Q-learning but offers potential advantages where reducing update variance is beneficial:\n",
    "\n",
    "1.  **Stochastic Environments**: Where the outcome of an action or the rewards are noisy, averaging over possible next actions can lead to smoother learning.\n",
    "2.  **Robotics & Control**: Similar to SARSA, useful for learning safe policies, potentially with faster convergence due to lower variance.\n",
    "3.  **Any scenario where SARSA is applicable but suffers from high variance in updates.**\n",
    "\n",
    "Expected SARSA works well under similar conditions as SARSA:\n",
    "- Discrete state and action spaces (in its tabular form).\n",
    "- An on-policy learning approach is desired.\n",
    "- Full observability of the environment state.\n",
    "\n",
    "## Mathematical Foundation of Expected SARSA\n",
    "\n",
    "### Complex Original Version\n",
    "\n",
    "The Expected SARSA algorithm updates Q-values using the following update rule:\n",
    "\n",
    "$$Q(s_t, a_t) \\leftarrow Q(s_t, a_t) + \\alpha \\left[r_t + \\gamma \\mathbb{E}_{\\pi}[Q(s_{t+1}, A')] - Q(s_t, a_t)\\right]$$\n",
    "\n",
    "Which expands to:\n",
    "\n",
    "$$Q(s_t, a_t) \\leftarrow Q(s_t, a_t) + \\alpha \\left[r_t + \\gamma \\sum_{a'} \\pi(a'|s_{t+1}) Q(s_{t+1}, a') - Q(s_t, a_t)\\right]$$\n",
    "\n",
    "Where:\n",
    "- $Q(s_t, a_t)$ is the Q-value for state $s_t$ and action $a_t$.\n",
    "- $\\alpha$ is the learning rate (0 < $\\alpha$ ≤ 1).\n",
    "- $r_t$ is the reward received after taking action $a_t$ in state $s_t$.\n",
    "- $\\gamma$ is the discount factor (0 ≤ $\\gamma$ ≤ 1).\n",
    "- $s_{t+1}$ is the next state observed after taking action $a_t$.\n",
    "- $\\sum_{a'} \\pi(a'|s_{t+1}) Q(s_{t+1}, a')$ is the **expected Q-value** in the next state $s_{t+1}$. It's the sum of Q-values for all possible next actions $a'$, weighted by the probability $\\pi(a'|s_{t+1})$ of taking each action $a'$ in state $s_{t+1}$ according to the current policy $\\pi$.\n",
    "- The term in brackets is the TD error, based on the *expected* value of the next state under the current policy.\n",
    "\n",
    "If the policy $\\pi$ is epsilon-greedy with respect to the current Q-values:\n",
    "- Let $a^*_{s'} = \\arg\\max_{a''} Q(s', a'')$ be the greedy action in state $s'$.\n",
    "- The probability of choosing the greedy action is $\\pi(a^*_{s'}|s') = 1 - \\epsilon + \\frac{\\epsilon}{|\\mathcal{A}|}$.\n",
    "- The probability of choosing any non-greedy action $a' \\neq a^*_{s'}$ is $\\pi(a'|s') = \\frac{\\epsilon}{|\\mathcal{A}|}$.\n",
    "- Where $|\\mathcal{A}|$ is the number of actions available in state $s'$.\n",
    "\n",
    "The expected value term becomes:\n",
    "$$ \\mathbb{E}_{\\pi}[Q(s', A')] = (1 - \\epsilon + \\frac{\\epsilon}{|\\mathcal{A}|}) Q(s', a^*_{s'}) + \\sum_{a' \\neq a^*_{s'}} \\frac{\\epsilon}{|\\mathcal{A}|} Q(s', a') $$\n",
    "$$ = (1 - \\epsilon) Q(s', a^*_{s'}) + \\frac{\\epsilon}{|\\mathcal{A}|} \\sum_{a'} Q(s', a') $$\n",
    "\n",
    "### Simplified Version\n",
    "\n",
    "Simpler interpretation:\n",
    "\n",
    "$$\n",
    "Q_{\\text{new}} = Q_{\\text{old}} + \\alpha \\left[ R + \\gamma E[Q_{\\text{next}}] - Q_{\\text{old}} \\right]\n",
    "$$\n",
    "\n",
    "Where $E[Q_{\\text{next}}]$ is the expected Q-value in the next state, calculated by averaging over all possible actions according to the policy's probabilities.\n",
    "\n",
    "Or:\n",
    "\n",
    "$$\n",
    "Q_{\\text{new}} = Q_{\\text{old}} + \\alpha \\left[ \\text{Target} - Q_{\\text{old}} \\right]\n",
    "$$\n",
    "\n",
    "Where \"Target\" is $R + \\gamma E[Q_{\\text{next}}]$.\n",
    "\n",
    "## Step-by-Step Explanation of Expected SARSA\n",
    "\n",
    "1.  **Initialize Q-table**: Create a table Q(s, a) for all states s and actions a, typically with zeros.\n",
    "2.  **Loop for each episode**:\n",
    "    a.  Initialize state $s$.\n",
    "    b.  **Loop for each step of episode**:\n",
    "        i.  Choose action $a$ from state $s$ using a policy derived from Q (e.g., epsilon-greedy).\n",
    "        ii. Take action $a$, observe reward $r$ and next state $s'$.\n",
    "        iii. **Calculate Expected Q-value for $s'$**: Compute $E[Q(s', A')] = \\sum_{a'} \\pi(a'|s') Q(s', a')$.\n",
    "        iv. **Update Q-value**: Apply the Expected SARSA update rule:\n",
    "            $Q(s, a) \\leftarrow Q(s, a) + \\alpha [r + \\gamma E[Q(s', A')] - Q(s, a)]$\n",
    "        v.  Update state: $s \\leftarrow s'$.\n",
    "        vi. If $s$ is terminal, end the episode.\n",
    "3.  **Repeat**: Continue running episodes until Q-values converge or a maximum number of episodes is reached.\n",
    "\n",
    "*Note*: Unlike SARSA, the action chosen *from* state $s'$ (which would be $a'$ in SARSA) is not directly used in the update of $Q(s, a)$. The update uses the *expectation* over all actions from $s'$. The action $a'$ is still chosen to determine the *next* state transition for the subsequent step.\n",
    "\n",
    "## Key Components of Expected SARSA\n",
    "\n",
    "### Q-Table\n",
    "Same structure as in Q-Learning and SARSA, storing state-action values.\n",
    "\n",
    "### Exploration vs. Exploitation (Policy)\n",
    "Expected SARSA is on-policy, meaning the policy used for updates is the same as the policy used for generating behavior. The **epsilon-greedy** strategy is commonly used. The calculation of the expected value explicitly uses the epsilon value and the Q-values to determine the probability of each action.\n",
    "\n",
    "### Learning Rate (α)\n",
    "Controls the step size for Q-value updates. Same role as in Q-Learning/SARSA.\n",
    "\n",
    "### Discount Factor (γ)\n",
    "Determines the present value of future rewards. Same role as in Q-Learning/SARSA.\n",
    "\n",
    "## Expected SARSA vs. SARSA vs. Q-Learning\n",
    "\n",
    "| Feature           | Q-Learning                                           | SARSA                                      | Expected SARSA                                                              |\n",
    "| :---------------- | :--------------------------------------------------- | :----------------------------------------- | :-------------------------------------------------------------------------- |\n",
    "| **Type**          | Off-Policy                                           | On-Policy                                  | On-Policy                                                                   |\n",
    "| **Update Target** | $r + \\gamma \\max_{a'} Q(s', a')$                      | $r + \\gamma Q(s', a')$                     | $r + \\gamma \\sum_{a'} \\pi(a'\\mid s') Q(s', a')$ |\n",
    "| **Basis**         | Learns optimal value function                        | Learns value function of current policy    | Learns value function of current policy                                     |\n",
    "| **Exploration**   | Learns optimal path independent of exploration choices | Update depends on the exploratory action $a'$ | Update averages over exploration, reducing variance from the choice of $a'$ |\n",
    "| **Variance**      | Can have high variance (max operator)                | High variance (depends on single sampled $a'$) | Lower variance than SARSA (uses expectation)                                |\n",
    "| **Bias**          | Potential maximization bias                          | Less bias than Q-learning                  | Less bias than Q-learning                                                   |\n",
    "| **Behavior**      | Can be more aggressive/optimal                       | Often more conservative/safer              | Often more stable/smoother learning than SARSA, behavior similar to SARSA   |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Environment\n",
    "Import necessary libraries including NumPy for numerical operations and Matplotlib for visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np  # For numerical operations\n",
    "import matplotlib.pyplot as plt  # For visualizations\n",
    "\n",
    "# Import type hints\n",
    "from typing import List, Tuple, Dict, Optional\n",
    "\n",
    "# set seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Enable inline plotting for Jupyter Notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Simple Environment\n",
    "\n",
    "To create a simple environment for the expected SARSA algorithm, we will define a 4x4 GridWorld. The GridWorld will have the following properties:\n",
    "\n",
    "- 4 rows and 4 columns\n",
    "- Possible actions: 'up', 'down', 'left', 'right'\n",
    "- Specific terminal states with rewards.\n",
    "- Cliff states (in the Cliff Walking example later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the GridWorld environment creator function\n",
    "def create_gridworld(\n",
    "    rows: int,\n",
    "    cols: int,\n",
    "    terminal_states: List[Tuple[int, int]],\n",
    "    rewards: Dict[Tuple[int, int], int]\n",
    ") -> Tuple[np.ndarray, List[Tuple[int, int]], List[str]]:\n",
    "    \"\"\"\n",
    "    Create a simple GridWorld environment.\n",
    "\n",
    "    Parameters:\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "    - terminal_states (List[Tuple[int, int]]): List of terminal states as (row, col) tuples.\n",
    "    - rewards (Dict[Tuple[int, int], int]): Dictionary mapping (row, col) to reward values.\n",
    "\n",
    "    Returns:\n",
    "    - grid (np.ndarray): A 2D array representing the grid with rewards (for reference, not used by agent).\n",
    "    - state_space (List[Tuple[int, int]]): List of all possible states in the grid.\n",
    "    - action_space (List[str]): List of possible actions ('up', 'down', 'left', 'right').\n",
    "    \"\"\"\n",
    "    # Initialize the grid with zeros (for visualization/reference)\n",
    "    grid = np.zeros((rows, cols))\n",
    "\n",
    "    # Assign rewards to specified states\n",
    "    for (row, col), reward in rewards.items():\n",
    "        grid[row, col] = reward\n",
    "\n",
    "    # Define the state space as all possible (row, col) pairs\n",
    "    state_space = [\n",
    "        (row, col)\n",
    "        for row in range(rows)\n",
    "        for col in range(cols)\n",
    "    ]\n",
    "\n",
    "    # Define the action space as the four possible movements\n",
    "    action_space = ['up', 'down', 'left', 'right']\n",
    "\n",
    "    return grid, state_space, action_space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need the state transition function, which takes the current state and action as input and returns the next state. Think of this as the agent moving around the grid based on the action it takes. The environment is deterministic in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define state transition function\n",
    "def state_transition(state: Tuple[int, int], action: str, rows: int, cols: int) -> Tuple[int, int]:\n",
    "    \"\"\"\n",
    "    Compute the next state given the current state and action. Handles boundaries.\n",
    "\n",
    "    Parameters:\n",
    "    - state (Tuple[int, int]): Current state as (row, col).\n",
    "    - action (str): Action to take ('up', 'down', 'left', 'right').\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "\n",
    "    Returns:\n",
    "    - Tuple[int, int]: The resulting state (row, col) after taking the action.\n",
    "    \"\"\"\n",
    "    # Unpack the current state into row and column\n",
    "    row, col = state\n",
    "    next_row, next_col = row, col\n",
    "\n",
    "    # Update the row or column based on the action, ensuring boundaries are respected\n",
    "    if action == 'up' and row > 0:  # Move up if not in the topmost row\n",
    "        next_row -= 1\n",
    "    elif action == 'down' and row < rows - 1:  # Move down if not in the bottommost row\n",
    "        next_row += 1\n",
    "    elif action == 'left' and col > 0:  # Move left if not in the leftmost column\n",
    "        next_col -= 1\n",
    "    elif action == 'right' and col < cols - 1:  # Move right if not in the rightmost column\n",
    "        next_col += 1\n",
    "    # If action would move off grid, stay in the same state (row, col remain unchanged)\n",
    "\n",
    "    # Return the new state as a tuple\n",
    "    return (next_row, next_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our agent can interact with the environment, we need to define the reward function. This function will return the reward for arriving in a given state, which will be used to update the Q-values during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define reward function\n",
    "def get_reward(state: Tuple[int, int], rewards: Dict[Tuple[int, int], int]) -> int:\n",
    "    \"\"\"\n",
    "    Get the reward for a given state.\n",
    "\n",
    "    Parameters:\n",
    "    - state (Tuple[int, int]): Current state as (row, col).\n",
    "    - rewards (Dict[Tuple[int, int], int]): Dictionary mapping state (row, col) to reward values.\n",
    "\n",
    "    Returns:\n",
    "    - int: The reward for the given state. Returns 0 if the state is not in the rewards dictionary.\n",
    "    \"\"\"\n",
    "    # Use the rewards dictionary to fetch the reward for the given state.\n",
    "    # If the state is not found, return a default reward of 0.\n",
    "    return rewards.get(state, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have defined the GridWorld environment and the necessary helper functions, let's test them with a simple example. We will create a 4x4 grid with two terminal states at (0, 0) and (3, 3) having rewards of 1 and 10, respectively. We will then test the state transition and reward functions by moving from the state (2, 2) upwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridWorld (rewards view):\n",
      "[[ 1.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.]\n",
      " [ 0.  0.  0. 10.]]\n",
      "Current State: (2, 2)\n",
      "Action Taken: up\n",
      "Next State: (1, 2)\n",
      "Reward at Next State: 0\n"
     ]
    }
   ],
   "source": [
    "# Example usage of the GridWorld environment\n",
    "\n",
    "# Define the grid dimensions (4x4), terminal states, and rewards\n",
    "rows, cols = 4, 4  # Number of rows and columns in the grid\n",
    "terminal_states = [(0, 0), (3, 3)]  # Terminal states\n",
    "rewards = {(0, 0): 1, (3, 3): 10}  # Rewards for terminal states (other states have reward 0)\n",
    "\n",
    "# Create the GridWorld environment\n",
    "grid, state_space, action_space = create_gridworld(rows, cols, terminal_states, rewards)\n",
    "\n",
    "# Test the state transition and reward functions\n",
    "current_state = (2, 2)  # Starting state\n",
    "action = 'up'  # Action to take\n",
    "next_state = state_transition(current_state, action, rows, cols)  # Compute the next state\n",
    "reward = get_reward(next_state, rewards)  # Get the reward for the next state\n",
    "\n",
    "# Print the results\n",
    "print(\"GridWorld (rewards view):\")  # Display the grid with rewards\n",
    "print(grid)\n",
    "print(f\"Current State: {current_state}\")  # Display the current state\n",
    "print(f\"Action Taken: {action}\")  # Display the action taken\n",
    "print(f\"Next State: {next_state}\")  # Display the resulting next state\n",
    "print(f\"Reward at Next State: {reward}\")  # Display the reward for the next state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the GridWorld environment is created with the specified dimensions, terminal states, and rewards. We selected a starting state (2, 2) and an action ('up'). The next state is computed as (1, 2), and the reward for arriving in state (1, 2) is 0 because it's not a specified reward state.\n",
    "\n",
    "# Implementing the Expected SARSA Algorithm\n",
    "\n",
    "We have successfully implemented the GridWorld environment. Now we implement the core components: Q-table initialization, epsilon-greedy policy, and the specific Expected SARSA update rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Q-table\n",
    "def initialize_q_table(state_space: List[Tuple[int, int]], action_space: List[str]) -> Dict[Tuple[Tuple[int, int], str], float]:\n",
    "    \"\"\"\n",
    "    Initialize the Q-table with zeros for all state-action pairs.\n",
    "    Uses a single dictionary with (state, action) tuples as keys.\n",
    "\n",
    "    Parameters:\n",
    "    - state_space (List[Tuple[int, int]]): List of all possible states.\n",
    "    - action_space (List[str]): List of all possible actions.\n",
    "\n",
    "    Returns:\n",
    "    - q_table (Dict[Tuple[Tuple[int, int], str], float]): A dictionary mapping (state, action) pairs to Q-values, initialized to 0.0.\n",
    "    \"\"\"\n",
    "    q_table: Dict[Tuple[Tuple[int, int], str], float] = {}\n",
    "    for state in state_space:\n",
    "        for action in action_space:\n",
    "            # Initialize Q-value for the (state, action) pair to 0.0\n",
    "            q_table[(state, action)] = 0.0\n",
    "    return q_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Alternative Q-Table Structure (Nested Dictionary) ---\n",
    "# for easier comparison and reuse. The run_sarsa_episode will adapt.\n",
    "def initialize_q_table_nested(state_space: List[Tuple[int, int]], action_space: List[str]) -> Dict[Tuple[int, int], Dict[str, float]]:\n",
    "    \"\"\"\n",
    "    Initialize the Q-table with zeros using a nested dictionary structure.\n",
    "\n",
    "    Parameters:\n",
    "    - state_space (List[Tuple[int, int]]): List of all possible states.\n",
    "    - action_space (List[str]): List of all possible actions.\n",
    "\n",
    "    Returns:\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): A nested dictionary where q_table[state][action] gives the Q-value.\n",
    "    \"\"\"\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]] = {}\n",
    "    for state in state_space:\n",
    "        # For terminal states, Q-values should remain 0 as no actions can be taken from them.\n",
    "        # However, initializing all allows easier lookup during updates before termination check.\n",
    "        q_table[state] = {action: 0.0 for action in action_space}\n",
    "    return q_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will implement the epsilon-greedy policy for action selection. This policy balances exploration and exploitation by choosing a random action with probability epsilon and the best action (highest Q-value) with probability 1 - epsilon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose action using epsilon-greedy policy\n",
    "def epsilon_greedy_policy(\n",
    "    state: Tuple[int, int],  # Current state as a tuple (row, col)\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]],  # Q-table as a nested dictionary\n",
    "    action_space: List[str],  # List of possible actions\n",
    "    epsilon: float  # Exploration rate (probability of choosing a random action)\n",
    ") -> str:\n",
    "    \"\"\"\n",
    "    Chooses an action using the epsilon-greedy policy.\n",
    "\n",
    "    Parameters:\n",
    "    - state (Tuple[int, int]): The current state of the agent.\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): The Q-table mapping states to actions and their Q-values.\n",
    "    - action_space (List[str]): The list of all possible actions.\n",
    "    - epsilon (float): The exploration rate (0 <= epsilon <= 1).\n",
    "\n",
    "    Returns:\n",
    "    - str: The chosen action.\n",
    "    \"\"\"\n",
    "    # If the state is not in the Q-table, choose a random action\n",
    "    if state not in q_table:\n",
    "        return np.random.choice(action_space)\n",
    "\n",
    "    # With probability epsilon, choose a random action (exploration)\n",
    "    if np.random.rand() < epsilon:\n",
    "        return np.random.choice(action_space)\n",
    "    else:\n",
    "        # Otherwise, choose the action with the highest Q-value (exploitation)\n",
    "        if q_table[state]:  # Ensure the state has valid Q-values\n",
    "            # Find the maximum Q-value for the current state\n",
    "            max_q = max(q_table[state].values())\n",
    "            # Find all actions with the maximum Q-value (to handle ties)\n",
    "            best_actions = [action for action, q in q_table[state].items() if q == max_q]\n",
    "            # Randomly choose among the best actions in case of ties\n",
    "            return np.random.choice(best_actions)\n",
    "        else:\n",
    "            # If no Q-values are available for the state, choose a random action\n",
    "            return np.random.choice(action_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's code the logic of how the agent updates its Q-values using the Expected SARSA algorithm. It will use the Q-table to calculate the expected value of the next state based on the current policy (epsilon-greedy). The Q-value update will be based on the reward received and the expected value of the next state, adjusted by the learning rate (alpha) and discount factor (gamma)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update Q-value using Expected SARSA rule\n",
    "def update_expected_sarsa_value(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]],  # Q-table mapping states to actions and their Q-values\n",
    "    state: Tuple[int, int],  # Current state as a tuple (row, col)\n",
    "    action: str,  # Action taken in the current state\n",
    "    reward: int,  # Reward received after taking the action\n",
    "    next_state: Tuple[int, int],  # Next state reached after taking the action\n",
    "    alpha: float,  # Learning rate (step size)\n",
    "    gamma: float,  # Discount factor (importance of future rewards)\n",
    "    epsilon: float,  # Exploration rate (used to calculate policy probabilities)\n",
    "    action_space: List[str],  # List of all possible actions\n",
    "    terminal_states: List[Tuple[int, int]]  # List of terminal states\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Updates the Q-value for a given state-action pair using the Expected SARSA rule.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table: The Q-table storing Q-values for all state-action pairs.\n",
    "    - state: The current state as a tuple (row, col).\n",
    "    - action: The action taken in the current state.\n",
    "    - reward: The reward received after taking the action.\n",
    "    - next_state: The next state reached after taking the action.\n",
    "    - alpha: The learning rate (0 < alpha <= 1).\n",
    "    - gamma: The discount factor (0 <= gamma <= 1).\n",
    "    - epsilon: The exploration rate (0 <= epsilon <= 1).\n",
    "    - action_space: The list of all possible actions.\n",
    "    - terminal_states: The list of terminal states in the environment.\n",
    "\n",
    "    Returns:\n",
    "    - None: Updates the Q-value in the Q-table in place.\n",
    "    \"\"\"\n",
    "    # Ensure the current state and action exist in the Q-table\n",
    "    if state not in q_table or action not in q_table[state]:\n",
    "        return\n",
    "\n",
    "    # Initialize the expected Q-value for the next state\n",
    "    expected_q_next: float = 0.0\n",
    "\n",
    "    # If the next state is not terminal, calculate the expected Q-value\n",
    "    if next_state not in terminal_states and next_state in q_table and q_table[next_state]:\n",
    "        # Get the Q-values for all actions in the next state\n",
    "        q_values_next_state: Dict[str, float] = q_table[next_state]\n",
    "\n",
    "        # Find the maximum Q-value in the next state\n",
    "        max_q_next: float = max(q_values_next_state.values())\n",
    "\n",
    "        # Identify all actions with the maximum Q-value (greedy actions)\n",
    "        greedy_actions: List[str] = [a for a, q in q_values_next_state.items() if q == max_q_next]\n",
    "\n",
    "        # Calculate the probabilities for greedy and non-greedy actions\n",
    "        num_actions: int = len(action_space)\n",
    "        num_greedy_actions: int = len(greedy_actions)\n",
    "        prob_greedy: float = (1.0 - epsilon) / num_greedy_actions + epsilon / num_actions\n",
    "        prob_non_greedy: float = epsilon / num_actions\n",
    "\n",
    "        # Compute the expected Q-value for the next state\n",
    "        for a_prime in action_space:\n",
    "            q_s_prime_a_prime: float = q_values_next_state.get(a_prime, 0.0)\n",
    "            if a_prime in greedy_actions:\n",
    "                expected_q_next += prob_greedy * q_s_prime_a_prime\n",
    "            else:\n",
    "                expected_q_next += prob_non_greedy * q_s_prime_a_prime\n",
    "\n",
    "    # If the next state is terminal, expected_q_next remains 0.0\n",
    "\n",
    "    # Calculate the Temporal Difference (TD) target and error\n",
    "    # TD Target: td_target = reward + gamma * expected_q_next\n",
    "    # TD Error: td_error = td_target - q_table[state][action]\n",
    "    td_target: float = reward + gamma * expected_q_next\n",
    "    td_error: float = td_target - q_table[state][action]\n",
    "\n",
    "    # Update the Q-value for the current state-action pair\n",
    "    # Q(s, a) <- Q(s, a) + alpha * td_error\n",
    "    q_table[state][action] += alpha * td_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have defined the environment and the core Expected SARSA Q Value update logic. Now, we combine these into a function to run a single episode using the Expected SARSA update logic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run a single episode of Expected SARSA\n",
    "def run_expected_sarsa_episode(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]],  # Q-table mapping states to actions and their Q-values\n",
    "    state_space: List[Tuple[int, int]],  # List of all possible states in the environment\n",
    "    action_space: List[str],  # List of all possible actions\n",
    "    rewards: Dict[Tuple[int, int], int],  # Dictionary mapping states to their rewards\n",
    "    terminal_states: List[Tuple[int, int]],  # List of terminal states in the environment\n",
    "    rows: int,  # Number of rows in the grid\n",
    "    cols: int,  # Number of columns in the grid\n",
    "    alpha: float,  # Learning rate (0 < alpha <= 1)\n",
    "    gamma: float,  # Discount factor (0 <= gamma <= 1)\n",
    "    epsilon: float,  # Exploration rate (0 <= epsilon <= 1)\n",
    "    max_steps: int  # Maximum number of steps per episode\n",
    ") -> Tuple[int, int]:\n",
    "    \"\"\"\n",
    "    Runs a single episode using Expected SARSA updates.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table: The Q-table storing Q-values for all state-action pairs.\n",
    "    - state_space: List of all possible states in the environment.\n",
    "    - action_space: List of all possible actions.\n",
    "    - rewards: Dictionary mapping states to their rewards.\n",
    "    - terminal_states: List of terminal states in the environment.\n",
    "    - rows: Number of rows in the grid.\n",
    "    - cols: Number of columns in the grid.\n",
    "    - alpha: The learning rate (0 < alpha <= 1).\n",
    "    - gamma: The discount factor (0 <= gamma <= 1).\n",
    "    - epsilon: The exploration rate (0 <= epsilon <= 1).\n",
    "    - max_steps: The maximum number of steps allowed per episode.\n",
    "\n",
    "    Returns:\n",
    "    - Tuple[int, int]: Total reward accumulated during the episode and the number of steps taken.\n",
    "    \"\"\"\n",
    "    # Initialize the starting state randomly, ensuring it is not a terminal state\n",
    "    state: Tuple[int, int] = state_space[np.random.choice(len(state_space))]\n",
    "    while state in terminal_states:\n",
    "        state = state_space[np.random.choice(len(state_space))]\n",
    "\n",
    "    total_reward: int = 0  # Accumulate total reward for the episode\n",
    "    steps: int = 0  # Track the number of steps taken in the episode\n",
    "\n",
    "    for _ in range(max_steps):\n",
    "        # Choose an action using the epsilon-greedy policy\n",
    "        action: str = epsilon_greedy_policy(state, q_table, action_space, epsilon)\n",
    "\n",
    "        # Take the chosen action, observe the next state and reward\n",
    "        next_state: Tuple[int, int] = state_transition(state, action, rows, cols)\n",
    "        reward: int = get_reward(next_state, rewards)\n",
    "        total_reward += reward  # Update the total reward\n",
    "\n",
    "        # Update the Q-value for the current state-action pair using Expected SARSA\n",
    "        update_expected_sarsa_value(\n",
    "            q_table, state, action, reward, next_state, alpha, gamma, epsilon, action_space, terminal_states\n",
    "        )\n",
    "\n",
    "        # Move to the next state\n",
    "        state = next_state\n",
    "        steps += 1  # Increment the step counter\n",
    "\n",
    "        # Terminate the episode if a terminal state is reached\n",
    "        if state in terminal_states:\n",
    "            break\n",
    "\n",
    "    return total_reward, steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration vs. Exploitation Strategy\n",
    "\n",
    "To properly balance exploration and exploitation, we use the epsilon-greedy policy defined earlier and implement dynamic epsilon adjustment. Epsilon starts high (more exploration) and gradually decays, encouraging more exploitation as the agent learns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define dynamic epsilon adjustment function (same as in Q-Learning/sarsa reference)\n",
    "def adjust_epsilon(\n",
    "    initial_epsilon: float,\n",
    "    min_epsilon: float,\n",
    "    decay_rate: float,\n",
    "    episode: int\n",
    ") -> float:\n",
    "    \"\"\"\n",
    "    Dynamically adjust epsilon over time using exponential decay.\n",
    "\n",
    "    Parameters:\n",
    "    - initial_epsilon (float): Initial exploration rate.\n",
    "    - min_epsilon (float): Minimum exploration rate.\n",
    "    - decay_rate (float): Rate at which epsilon decays.\n",
    "    - episode (int): Current episode number.\n",
    "\n",
    "    Returns:\n",
    "    - float: Adjusted exploration rate for the current episode.\n",
    "    \"\"\"\n",
    "    # Compute the decayed epsilon value, ensuring it doesn't go below the minimum epsilon\n",
    "    return max(min_epsilon, initial_epsilon * np.exp(-decay_rate * episode))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's track and plot the epsilon decay over the planned episodes to visualize the exploration strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example usage of dynamic epsilon adjustment and plotting the decay\n",
    "\n",
    "# Define epsilon parameters\n",
    "initial_epsilon: float = 1.0  # Start with full exploration\n",
    "min_epsilon: float = 0.1  # Minimum exploration rate\n",
    "decay_rate: float = 0.01  # Decay rate for epsilon\n",
    "episodes: int = 500  # Total number of episodes for training\n",
    "\n",
    "# Track epsilon values over episodes\n",
    "epsilon_values: List[float] = []\n",
    "for episode in range(episodes):\n",
    "    # Adjust epsilon for the current episode\n",
    "    current_epsilon = adjust_epsilon(initial_epsilon, min_epsilon, decay_rate, episode)\n",
    "    epsilon_values.append(current_epsilon)\n",
    "    \n",
    "\n",
    "# Plot epsilon decay over episodes\n",
    "plt.figure(figsize=(20, 3)) # Adjusted figure size slightly\n",
    "plt.plot(epsilon_values)\n",
    "plt.xlabel('Episode')  # Label for the x-axis\n",
    "plt.ylabel('Epsilon')  # Label for the y-axis\n",
    "plt.title('Epsilon Decay Over Episodes')  # Title of the plot\n",
    "plt.grid(True) # Add grid for better readability\n",
    "plt.show()  # Display the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows epsilon starting at 1.0 (pure exploration) and decaying exponentially towards the minimum value of 0.1 (mostly exploitation with 10% exploration). This gradual shift allows the agent to discover the environment initially and then refine its policy based on what it has learned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the Expected SARSA Algorithm\n",
    "\n",
    "Now we run the Expected SARSA algorithm over multiple episodes in the GridWorld environment, using the dynamic epsilon adjustment. We will track the total reward and episode length for each episode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to execute Expected SARSA over multiple episodes\n",
    "def run_expected_sarsa(\n",
    "    state_space: List[Tuple[int, int]],  # List of all possible states in the environment\n",
    "    action_space: List[str],  # List of all possible actions\n",
    "    rewards: Dict[Tuple[int, int], int],  # Dictionary mapping states to their rewards\n",
    "    terminal_states: List[Tuple[int, int]],  # List of terminal states in the environment\n",
    "    rows: int,  # Number of rows in the grid\n",
    "    cols: int,  # Number of columns in the grid\n",
    "    alpha: float,  # Learning rate (0 < alpha <= 1)\n",
    "    gamma: float,  # Discount factor (0 <= gamma <= 1)\n",
    "    initial_epsilon: float,  # Initial exploration rate (0 <= epsilon <= 1)\n",
    "    min_epsilon: float,  # Minimum exploration rate\n",
    "    decay_rate: float,  # Rate at which epsilon decays\n",
    "    episodes: int,  # Number of episodes to train\n",
    "    max_steps: int  # Maximum number of steps per episode\n",
    ") -> Tuple[Dict[Tuple[int, int], Dict[str, float]], List[int], List[int]]:\n",
    "    \"\"\"\n",
    "    Executes the Expected SARSA algorithm over multiple episodes.\n",
    "\n",
    "    Parameters:\n",
    "    - state_space: List of all possible states in the environment.\n",
    "    - action_space: List of all possible actions.\n",
    "    - rewards: Dictionary mapping states to their rewards.\n",
    "    - terminal_states: List of terminal states in the environment.\n",
    "    - rows: Number of rows in the grid.\n",
    "    - cols: Number of columns in the grid.\n",
    "    - alpha: Learning rate (0 < alpha <= 1).\n",
    "    - gamma: Discount factor (0 <= gamma <= 1).\n",
    "    - initial_epsilon: Initial exploration rate (0 <= epsilon <= 1).\n",
    "    - min_epsilon: Minimum exploration rate.\n",
    "    - decay_rate: Rate at which epsilon decays.\n",
    "    - episodes: Number of episodes to train.\n",
    "    - max_steps: Maximum number of steps per episode.\n",
    "\n",
    "    Returns:\n",
    "    - Tuple containing:\n",
    "        - q_table: A nested dictionary mapping states to actions and their Q-values.\n",
    "        - rewards_per_episode: List of total rewards accumulated in each episode.\n",
    "        - episode_lengths: List of the number of steps taken in each episode.\n",
    "    \"\"\"\n",
    "    # Initialize the Q-table with zeros for all state-action pairs\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]] = initialize_q_table_nested(state_space, action_space)\n",
    "\n",
    "    # Lists to store rewards and episode lengths for each episode\n",
    "    rewards_per_episode: List[int] = []\n",
    "    episode_lengths: List[int] = []\n",
    "\n",
    "    # Loop through each episode\n",
    "    for episode in range(episodes):\n",
    "        # Dynamically adjust epsilon for the current episode\n",
    "        epsilon: float = adjust_epsilon(initial_epsilon, min_epsilon, decay_rate, episode)\n",
    "\n",
    "        # Run a single episode of Expected SARSA\n",
    "        total_reward, steps = run_expected_sarsa_episode(\n",
    "            q_table, state_space, action_space, rewards, terminal_states,\n",
    "            rows, cols, alpha, gamma, epsilon, max_steps\n",
    "        )\n",
    "\n",
    "        # Store the total reward and episode length\n",
    "        rewards_per_episode.append(total_reward)\n",
    "        episode_lengths.append(steps)\n",
    "\n",
    "    # Return the Q-table, rewards per episode, and episode lengths\n",
    "    return q_table, rewards_per_episode, episode_lengths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the hyperparameters and run the SARSA training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Expected SARSA...\n",
      "Expected SARSA training completed.\n"
     ]
    }
   ],
   "source": [
    "# --- Run Expected SARSA ---\n",
    "# Define hyperparameters for Expected SARSA\n",
    "alpha_es = 0.1  # Learning rate: controls the step size for Q-value updates\n",
    "gamma_es = 0.9  # Discount factor: determines the importance of future rewards\n",
    "initial_epsilon_es = 1.0  # Initial exploration rate: probability of choosing a random action\n",
    "min_epsilon_es = 0.1  # Minimum exploration rate: lower bound for epsilon\n",
    "decay_rate_es = 0.01  # Epsilon decay rate: controls how quickly epsilon decreases\n",
    "episodes_es = 500  # Number of episodes to train the agent\n",
    "max_steps_es = 100  # Maximum number of steps allowed per episode\n",
    "\n",
    "# Print a message indicating the start of training\n",
    "print(\"Running Expected SARSA...\")\n",
    "\n",
    "# Run the Expected SARSA algorithm with the specified hyperparameters\n",
    "es_q_table, es_rewards_per_episode, es_episode_lengths = run_expected_sarsa(\n",
    "    state_space, action_space, rewards, terminal_states, rows, cols, alpha_es, gamma_es,\n",
    "    initial_epsilon_es, min_epsilon_es, decay_rate_es, episodes_es, max_steps_es\n",
    ")\n",
    "\n",
    "# Print a message indicating the completion of training\n",
    "print(\"Expected SARSA training completed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Learning Process\n",
    "\n",
    "Let's visualize the training progress by plotting the total rewards per episode and the length of each episode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results for Expected SARSA\n",
    "plt.figure(figsize=(20, 3))\n",
    "\n",
    "# Rewards\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(es_rewards_per_episode)\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Total Reward')\n",
    "plt.title('Expected SARSA: Rewards Over Episodes')\n",
    "plt.grid(True)\n",
    "\n",
    "# Episode Lengths\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(es_episode_lengths)\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Episode Length')\n",
    "plt.title('Expected SARSA: Episode Lengths Over Episodes')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of Expected SARSA Learning Curves**\n",
    "\n",
    "**Rewards Curve (Left)**\n",
    "- The reward curve appears highly volatile throughout the episodes, indicating frequent fluctuations in the agent's performance.\n",
    "- Unlike a smooth convergence, the rewards oscillate between low and high values, suggesting that the agent occasionally follows suboptimal paths or explores extensively.\n",
    "- The instability might be due to high exploration rates, an insufficiently tuned learning rate, or the nature of the environment introducing variability.\n",
    "- Compared to standard SARSA, the expected value update does not seem to significantly smooth out the fluctuations in this case.\n",
    "\n",
    "**Episode Length Curve (Right)**\n",
    "- Initially, episode lengths are high, confirming that the agent starts with inefficient strategies.\n",
    "- Over time, episode lengths decrease and stabilize at lower values, indicating that the agent is learning more optimal paths.\n",
    "- The overall trend is downward, suggesting successful learning, but some variance remains in later episodes.\n",
    "- Compared to the reward curve, this plot shows a clearer sign of learning progress, as shorter episode lengths correspond to more efficient goal-reaching behavior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Q-Values and Optimal Policy\n",
    "\n",
    "Now, we visualize the learned Q-values and the resulting policy derived from the Expected SARSA algorithm. We use heatmaps for Q-values per action and arrows on the grid for the policy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to visualize the Q-value heatmap\n",
    "def plot_q_values_heatmap(q_table: Dict[Tuple[int, int], Dict[str, float]], rows: int, cols: int, action_space: List[str], fig: plt.Figure, axes: np.ndarray) -> None:\n",
    "    \"\"\"\n",
    "    Visualize Q-values as heatmaps for each action on provided axes.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table: Nested dictionary mapping states to actions and their Q-values.\n",
    "    - rows: Number of rows in the grid.\n",
    "    - cols: Number of columns in the grid.\n",
    "    - action_space: List of possible actions (e.g., ['up', 'down', 'left', 'right']).\n",
    "    - fig: Matplotlib figure object.\n",
    "    - axes: Array of Matplotlib axes for plotting heatmaps.\n",
    "    \"\"\"\n",
    "    for i, action in enumerate(action_space):\n",
    "        # Initialize a grid with -inf for states not in the Q-table\n",
    "        q_values = np.full((rows, cols), -np.inf)\n",
    "        for (row, col), actions in q_table.items():\n",
    "            if action in actions:\n",
    "                q_values[row, col] = actions[action]  # Assign Q-value for the action\n",
    "\n",
    "        # Plot the heatmap for the current action\n",
    "        ax = axes[i]\n",
    "        cax = ax.matshow(q_values, cmap='viridis')  # Use 'viridis' colormap\n",
    "        fig.colorbar(cax, ax=ax)  # Add a colorbar to the heatmap\n",
    "        ax.set_title(f\"Expected SARSA Q-values: {action}\")  # Title for the heatmap\n",
    "\n",
    "        # Add gridlines for better visualization\n",
    "        ax.set_xticks(np.arange(-.5, cols, 1), minor=True)\n",
    "        ax.set_yticks(np.arange(-.5, rows, 1), minor=True)\n",
    "        ax.grid(which='minor', color='w', linestyle='-', linewidth=1)\n",
    "\n",
    "        # Remove tick labels for a cleaner look\n",
    "        ax.set_xticks(np.arange(cols))\n",
    "        ax.set_yticks(np.arange(rows))\n",
    "        ax.tick_params(axis='both', which='both', length=0)\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "\n",
    "\n",
    "# Function to visualize the learned policy\n",
    "def plot_policy_grid(q_table: Dict[Tuple[int, int], Dict[str, float]], rows: int, cols: int, terminal_states: List[Tuple[int, int]], ax: plt.Axes) -> None:\n",
    "    \"\"\"\n",
    "    Visualize the learned policy as arrows on the grid on a provided axis.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table: Nested dictionary mapping states to actions and their Q-values.\n",
    "    - rows: Number of rows in the grid.\n",
    "    - cols: Number of columns in the grid.\n",
    "    - terminal_states: List of terminal states in the grid.\n",
    "    - ax: Matplotlib axis for plotting the policy grid.\n",
    "    \"\"\"\n",
    "    # Initialize a grid to store the policy symbols\n",
    "    policy_grid = np.empty((rows, cols), dtype=str)\n",
    "    action_symbols = {'up': '↑', 'down': '↓', 'left': '←', 'right': '→', '': ''}\n",
    "\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            state = (r, c)\n",
    "            if state in terminal_states:\n",
    "                # Mark terminal states with 'T'\n",
    "                policy_grid[r, c] = 'T'\n",
    "                continue\n",
    "            if state in q_table and q_table[state]:\n",
    "                # Find the action(s) with the highest Q-value\n",
    "                max_q = -np.inf\n",
    "                best_actions = []\n",
    "                for action, q_val in q_table[state].items():\n",
    "                    if q_val > max_q:\n",
    "                        max_q = q_val\n",
    "                        best_actions = [action]\n",
    "                    elif q_val == max_q:\n",
    "                        best_actions.append(action)\n",
    "\n",
    "                # Choose the first action in case of ties\n",
    "                if best_actions:\n",
    "                    best_action = best_actions[0]\n",
    "                    policy_grid[r, c] = action_symbols[best_action]\n",
    "                else:\n",
    "                    # Mark states with no valid actions\n",
    "                    policy_grid[r, c] = '.'\n",
    "            else:\n",
    "                # Mark states not visited or without Q-values\n",
    "                policy_grid[r, c] = '.'\n",
    "\n",
    "    # Plot the policy grid\n",
    "    ax.matshow(np.zeros((rows, cols)), cmap='Greys', alpha=0.1)  # Background grid\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            # Add the policy symbol to each cell\n",
    "            ax.text(c, r, policy_grid[r, c], ha='center', va='center', fontsize=14, color='black' if policy_grid[r, c] != 'T' else 'red')\n",
    "\n",
    "    # Add gridlines and title\n",
    "    ax.set_title(\"Expected SARSA Learned Policy\")\n",
    "    ax.set_xticks(np.arange(-.5, cols, 1), minor=True)\n",
    "    ax.set_yticks(np.arange(-.5, rows, 1), minor=True)\n",
    "    ax.grid(which='minor', color='black', linestyle='-', linewidth=1)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Yr914bBx1F7/4qxVH9cS6+9Of/lS3b+LEiWnfVltt1eCxYh3Wn8oy1sfXvva18Pjjj6epMGMdrL322uHXv/51qATiBo2JL+JLuceXprz99tvpMWMZOnbsmMoUE0+Np3GO7Ys46q52OuP4uuL9qP40x5VO7KhM4oP4UM7xof7a2vG511lnnRQPXn755SWu2X3bbbelTg+xjJtssknqdNDU66l19dVX1z3utttumzpF1K+HSo4n4gYAVGiye/bs2eH9999vsMUpmmqdffbZqffmUUcdFT7++OO0L34Ivuaaa8K5554bNt988waPF3ufxvWG4vpAsQfoAw88EPbYY4+lfiBe2vQ+sVdqnCopX/HD/IEHHpgaQAcffHD417/+1eBD37LqIn7gjY2UxuWIX2S/+OKLeT3OO++8Ex555JH0/FG8/f3vfx8WLFiwzHPjlFNRnCaqKbHh8J3vfCc1DGLP4SWJH+BjD95bb711sZ/FhmBsvB100EHpfkzu3nnnnekL+Msuuyz11I1rQ8Xz42tpKbFBER97p512Sr2WjzjiiPR+xQZFbWMh9lD+yle+khoAZ555ZlqLKq6R1njtrrhWWmy8xS+TWlK83uJ6ebH+fvKTn6Sp6GJDe0m9sqEtEAf+Rxxoe3EgJhNiwjpeQ3EUzYUXXhjeeuutlPBuLK7ZHMsSyxCn/DvllFNSHcfyxvJE8cun+KVjHClT669//Wv6QjZ+ORdH/NR+sRbP3XnnnRs8R5zWMn4JGr+I/OlPf5qutfiF1EsvvVRAzUJpEl/+R3xpe/FlSYmemCCL8SROARzLEqcyjtd4THhEMS7UXncxFsR/xy2+htrpsmv3Zbk+oZyID/8jPrTd+HD99denx4qdNOLn/p49ezZ5XLx2v/Wtb6WpzWPHgngtxWs/vv9Nuemmm8Ill1ySXlts28Ryx3NqX494AgAkuQpx/fXXx65qTW4dO3ZscOwLL7yQ69ChQ+7oo4/Offjhh7l+/frlttlmm9znn39ed8wjjzySzo0/mzNnTt3+W2+9Ne0fM2ZM5jLOmDEjt9pqq6XzN9hgg9xxxx2Xu+mmm3IfffRRk8c/88wz6dgHHngg3a+urs71798/N2zYsMWOjccdddRRuVmzZuXee++9dO5Xv/rVtP+SSy5pcOz999+fa9++fdp22GGH3Omnn5677777cgsWLGiyHJdeemmuc+fOdfXw2muvpce94447lvma999//3RsrOcl+cMf/pCO+fnPf77Ux7rqqqvScfH9q2+jjTbK7b777nX3P/vss9yiRYsaHDN16tR0HZx//vkN9sXHi9dOrV122SVtjQ0ZMiQ3cODAuvt//etf07m//e1vGxx37733Ntgf6yjef/rpp5f62uLjx+NimZam9rqMt41fX+PXUvuYJ554Yt2+eA3tu+++6fqP1wq53OzZs1M9vf5q79x7b/dt9hbPj48TH4/iEAfEgUqIA3feeWc67ic/+UndvoULF+a+9KUvLfZatthii9zqq6+e++CDD+r2Pf/887l27drlDjvssLp9MS584QtfqLt/4IEHpi1eH/fcc0/aN2nSpPT4f/zjH+uOi/UR9z322GN1++K1F+v51FNPzbVV4kblEV/El0qIL1E8bsSIEXX34/vep0+f3Pvvv9/guG9/+9u57t275z755JMG5w4dOrTBcfF+BX0lsVRiR9skPogPlRAfasvcrVu39D439bP6r2fTTTdN18zHH39ct2/ChAnpuPqvp/bcXr165f7zn//U7Y/tjbj/z3/+c0XHE3Fj+dXxo48+WuyitFkTJ05MdRxvaR3quPWp49YX/w7nE6sqbmR3nNom9nqtv91zzz0NjomjmOI0RNdee23q/Rh73d5www2pd2djsSdo165d6+7H0Ut9+vQJd999d+ayxV6hcZTUcccdl3q6jhs3LvUmjVM2XXDBBYtNLR17Z8Zz4pRNUZymJ/aOvPnmm1PP3KbWYlpttdXS48X1ouIUWaeffnoayVVf7BH55JNPpumcYnniiN9YD/369WswjWn9csTpq2rrIU73FEcI5zOFVG2v5fp12Fjtz2qPXZLYszO+R7EHba3YKzhOnRTrpVac9iiOSItiPcUe1XEaqcGDB4dJkyaFlhCnZIrTx8a6rN97O9ZLfK7YAzmqnaLrL3/5y1KnhopTPsX3f0lTOhUijsKoFa+heD/2ho4jNPifRblcwRulQRwQB9pyHIjXXayD448/vm5fHFVy4oknLjYt4XPPPZdGWdcfdbHZZpulMte/fuM057Fe5s2bl+7Hacn32WefNAIpjvKO4m28/r74xS82eJ44PWE8v1a8/mI9x9EtbZ24UXnEF/GlLceXxuI5cb3c/fbbL/27flniexpHb7bUa64kYkfbJD6ID5UQH+J67PG9Xpo4ij2OaI/XcCxTrTjCPU7x3pRYj/VnAqhtW1RCeyIf4gYA1Ki4ZHdcMzJO71R/q/2QXl+c9idOFfWPf/wjTekcv6xtSvxAXV/8oB+nbqud/jOr2EAZO3Zs+hI6rvHz85//PH1YjFNXxUZCrfjhODYmYtmnTp2apgmNW1wrM04n13itpyhOaxobVXHKoDhdaSzrJ598UveBu764Bs4f/vCH1NiJdRCnL4of8mMjKn5orxXX9Xz22WfTFEm1ZYhbXE8ofnCund60kAZE7c9q12mKU3PNmDGjwRbFtYa+/OUvN5hCKjY4YsMjNkBqxalWf/azn6X3LjY44nmxjuO6pPFLmZYQp/GKjxXLHB+7/jZ37tw0bVTtB/rYIIiN2liO+B7FqZ/mz58flof43sf1U+tbf/31021zr+G2qroFNkqDOCAOtOU48Oabb6ZrqP6XR1H8Iq3xcU3tj+J0hfGLsdrkdvxCaeHChekLyHhNxrLHfXFq2vrJ7vg70ni6wjXXXHOxx49fVjVeN7AtEjcqj/givrTl+NLYrFmzwkcffZTWUW1cjjhtblRbFvIndrRN4oP4UAnxIa77vSy1bZB4vTbW1L6m2hO1ie9KaE/kQ9wAgBqLdxGlrodg/KAYxV6Hy1tsAMSkY9xib9X4gTj2UD366KPTzx9++OHUEIkNjbg1Fo+Na/DU179//9SoiuKIrPihNo7ijQ2V+h/C64trMMUGR9xiWeIXF7G3aO2azjfeeGO6Pfnkk9PWWOztX/tlR1Ni4y2uWxQ/4Dde57NW/FlUm5SNDYfGj1nb2/jb3/52+lkcrRZHnMUGR2x4xNda6+KLLw7nnHNOOPLII1NP5fjFfGxonXTSSakBsqz3pXHP5qhxD+b4OLGBsaRexbW9XePjxXWl4tpIcd2ouC5XLFdc3yjua5ysWJb4eE1pqoc1sHTiQA1xoLziQGuIo3A6deqU1u2OXzbF1xWvhZjw/uUvf5m+GIvJ7gMOOGCxc+Oo8qY0VYdQKcSXGuJLeceX2tdz6KGHhiFDhjR5TJwtBMif+FBDfCjP+NC5c+fQGrQnAIB8SHY3IX5AjNN6duvWLX3wjB9KY0/Spj6I1zZE6n/Yij1KW7JhHz9cx56LsVFRK354jR9i43RYjcWesHfccUeafmppHza/973vpZ6lZ599dvqCekmJ0vpfdke15Yiv9aabbkqNlO9///uLHR8/wMdyLq2REae9i/X761//uslGRvzwHp8jTpNV+/M4lVXsGdyU/fffP72u2imkXnvttdQbuL74oT6WuX4P5SiOTKjfGGlKfB+amiqptndqrXXWWSdNAx57GufzgX/77bdP20UXXZRe7yGHHJIaj7WNynzV9nCNr2Vp5at/rcfXUzuau7bOotaYMr2cLQq5tBVyPuVDHGiaOFD6cWDgwIFpVE0cvVH/i6o4SqfxcU3tj1599dVUD126dKn7wjGOSIoJ7Zjsrp06MN7GRHd8j+NoniV9WVipxA2aIr40TXwp/fjSVNIkjo6M9VibyMpqWddFJRI7Kpf40DTxofziw9LUtkHi9dpYU/vyVcnxRNwAgAqdxjwfl112WXjiiSfSlGzxg/KOO+6Y1r6MU3o2Fj8c15/6KH6AjR/C995777p98bz4xXGcqmlp/v73v9dNGVpfnL4prudTO9VonD4pNiS+9rWvpcZP4y32ko1lampdo/ritEqnnnpqmgLqj3/8Y93+uI5PUz0ka9d/qi3H3/72tzRNVmxENFWOuK5OfKy4Js+SxA/WsedvnDIpTjfV2I9+9KPUUIhrOtWuVRWn2Go8BVituPZQbITEnrTxQ3r8gj42PBr3Cm38+mIv4bfffjssS2w8xPcyTttXK64nFeuivm9+85upgRSvn8biVLC1yeg47VLjssSewFH9KaTiNRWfd2nrKdU2HOLri6Pv6osj75bkiiuuqPt3LEu8v+KKK6aeyPzPolzhG+VDHBAHyjUOxBEz8fHjVJS1Yjl+8YtfNDgu1mF8nrgWZP0OUnGNwfvvvz89Tn0xsR2vz/h+1ia74xdzccrzH//4x3XH8D/iBk0RX8SXco0vjcXXGqfBjSMoY+xorP7rWJLaTlWNO+pWMrGjcokP4kNbiQ9L07dv37Q+fbyGY+fcWo8++mhBsxlUcjwRNwCgQkd233PPPenDWmOxIRF7rsYP3HFqodijNvb2jMaPH58++MVeo/XX4Yni1ENf/OIX0wftOKrp8ssvT+vMHHPMMXXHxORhXAsnfuCOawgtyW9+85vUAzX2bt16663TB+RYnuuuuy5NH3rWWWel42LjITYi/u///m+JH9xjT/v4WPGD/tLE1xnXYYpfVNd+GD/xxBNTgyiWY4MNNggLFixIja7YSzWO9q3tIRsfP35gj9NbNSWWLzYS4of9U045ZYlliB9yd99997RO0He+8526kWKxITVhwoQ0NV5TU1MtSXzN8ZyY4I0NjtjwqC82zs4///z0OuL7Hj9Qx9fSeO3qpsSpnWIjND7uUUcdldY8ij2XN9544wbrQsU1kGLP3lGjRqWprGJDKiaQYw/s2KAZM2ZMaojFJEMsZ6zr2ICJ7+s111yTenPXTzTEXsHx2Lgu1tJGXHfv3j0cdNBBKakRe7bGx4yNtyWtlxevq3vvvTdNPRjX2Yq/H3EtrXit1U5xBW2NONCQONC24kC8ZuNojjPPPDN9ERinaYz12NRagJdcckn6UnSHHXZIryV+iRnjR4wlcU3F+uJ7Ekd9TJ8+vUFSO452ueqqq1KZ4jSVUMnEl4bEl7YVX5oyevTodO3FdkS8LmPM+c9//hMmTZqURhfGfy9NvBajH/zgB+l1x/c8TgcMbY340JD40Pbjw9LEUfWx3mObJdZHTL7H6zUmwesnwLMQTwCA2JuvIlx//fWxr9oSt/jzhQsX5rbddttc//79cx999FGD88eMGZOOu+WWW9L9Rx55JN3/3e9+lxs+fHhu9dVXz3Xu3Dm377775t58880G544YMSIdG89Zmn/+85+50047LbfVVlvlevbsmVthhRVyffr0yR100EG5SZMm1R2333775Tp16pSbN2/eEh/r8MMPz6244oq5999/P92Pzz906NAmjx05cmSD8t1zzz25I488MrfBBhvkVl555VyHDh1y6667bu7EE0/MzZw5Mx2zYMGCXK9evXJf+tKXlvqaBg0alNtyyy1zy/Lxxx/nzjvvvNzGG2+cXlvt+3LOOefkspozZ056L+L5N95442I//+yzz3Knnnpqqtt43E477ZR78sknc7vsskvaak2dOrXu2qgvPubaa6+d6mWLLbbI3XfffbkhQ4bkBg4cuNhzXX311bmtt946PU/Xrl1zm266ae7000/PvfPOO+nn8X09+OCDc2uuuWauY8eO6Tr62te+lnvmmWcaPE58/FiWWKZlmTVrVu4b3/hGbqWVVsr16NEj973vfS/34osvLvZa4mN26dIl9/rrr+e+8pWvpOPXWGONdL0uWrQoz9pu+2bPnp3q7rmXV8+9Pr13s7d4fnyc+HgUhzggDlRKHPjggw9y3/3ud3PdunXLde/ePf372WefbfK1PPjgg+n1x/LF4+O19fLLLzdZp+3bt0+vIf6e1K+L+LjxORqL9RF/HxprXM9tjbhRecQX8aVS4ks8Ll5z9cX3Lb7/AwYMSNdF7969c1/+8pdT+Rqf2/g6ib8X8b1fbbXVclVVVemYSiV2tE3ig/hQCfGhtsyXXHLJEn/W+PXcfPPN6b2Oz7/JJpvk/vSnP6XvseK+fB63cTyqxHgibiy/On700UeLXZQ2a+LEiamO4y2tQx23PnXc+uLf4XxiVVX8X7ET7uUo9vSM6+3E3pGxZyQtK07lFHu7xqmWnnzyybRGKC0r9qaO0501t+dspYg9peMox0kvrxFW7tr8lR/mflwdttpoZhpdGXtMU/7EgdYlDlCuxA0KJb60LvGFUiR2kA/xoXWJD8UVZzKIswMsaW10GhI3ll8dx2n244xmtLw4G1CcmWHixIlhq622KnZx2iR13PrUceuLS/bGGWyWFaus2U1J6tevX5pe+7PPPktTrMZpjaCYqnOFb0D+xAHKnbgBpUl8oZSJHVA84sPyEdcAjx0KGnfkiOuQL23KfZombgBAha7ZTfnYcMMNwwcffFDsYgBQJOIAAK1BfAGgKeLD8hlBv8cee6Q1zvv27ZvWs49rkPfu3Tscd9xxxS4eAFCmJLsB8rAoVKWtkPMBqBziBgBZiR1AW9ejR4803eu1114bZs2aFbp06RL23XffMHr06NCrV69iF6/siBsAUEOyu5ni1DqWO6ecjR8/Pm2UZgNi5MiR4bzzzmuwb/DgwanXM6VBHACWxhdPNJf4ApVL7GBpxAfagrgG8i233FLsYrQZ4gYA1JDsBihRG2+8cXjwwQfr7q+wgj/ZAAAAAAAAtWROAPJQnatKWyHnZxWT23HdKgDKTzHiBgDlTewAIAtxAwAyJLurq6vDO++8E7p27RqqqgRBoLTFqd0+/vjj0Ldv39CuXbuSmhpqzpw5DfZ37NgxbU3517/+lV5Dp06dwg477BBGjRoV1lxzzVAOxA2g0mOHKQWzEzuAclLKbY5KIW4A5UTcAIAiJ7tj42HAgAGtWAyAljd9+vTQv3//FnmsRaFd2pp/fo3Gf0tHjBiR1udubLvttktrqsd1ut999920fveXvvSl8OKLL6Yvc0qduAFUeuxoqbhRScQOoByVYpujUogbQDkSNwCgSMnu2sTK6fuPDO9P/U8rFKPt6Ld+3/DDa48Pl51yY3jnjfeKXZyS13ft1cMplx0afnrs1eHtKTOKXZyS1m/d3uHUq48Nl51wXXh7ysxiF6ekrbpm9zD6luElmRSOjZpu3brV3V/SqO6999677t+bbbZZSn4PHDgw3HrrreGoo44KpU7cyKb/BgPS77e/hRn+Fp54fXjndX8Ll6bvOmuEU35xRLjghd+HaZ+8X+zilLw1cl3CL/Y6oSRjR6UQO7K3OS68+r4wbcaHxS5OSVuzd49w9rF7hdGX3xPeekddLUv/vj3CmSftHS49emx4+1/vFrs4JW3VtXqEn9w5UtwoInEje9z46fdie8Nn6GXpt+4a4dSrar5/0ebIo81xxZHhstNvDu9MnVXs4pS0Xv1WDqOvP0ncAIBiJbtrp4OKjYdp/3ynNcrRZnRo3zElst6fNidMe8WHvGXpsEKnVF+zpn4Ypr3gy5R8rq1Z//4oTHtJYysfLTmVXa7AdZDi+VF8D+snu/O1yiqrhPXXXz9MmTIllANxI5uOHVf2tzBrnH1zdpj2sk5l+cTYGVXzwtRFEjzL1L5lY0dLxY1KInZk/1s448PPwtR3Py52cUpah46dU1299/5n4d/TGy4nw+I6dKipr1lv+D0s5zZHpRA3mvl9wovaG8vSYYV6bY6XtDnyaXO8/9bHYfprHxS7OGVB3ACAIiW7ASpdsddBmjt3bnj99dfDd7/73YIeB4DKiBsAlB+xA4AsxA0AqCHZDZCHRbl2aWv++dmO/+EPfxj222+/NHV5XIsuru3dvn37cPDBBze7DAC03bgBQPkTOwDIQtwAgBqS3QAl6K233kqJ7Q8++CCsttpq4Ytf/GJ46qmn0r8BAAAAAACQ7AbIS3WoCtWh+b1lq0O27rI333xzs58LgMqLGwCUP7EDgCzEDQCoIdkNkAfrIAGQhbgBQFZiBwBZiBsAUKP5Xb8AAAAAAAAAoEiM7AbIw6Jcu7Q1/3xTQwFUEnEDgKzEDgCyEDcqVFXGEfneZ+C/hg0bFm655ZYwY8aM0NZIdgPkvQ5S86d3KuRcAMqPuAFAVmIHAFmIGxVqxIjF911+eQizZzf9M4D/mj17dpg5c2ZoiyS7AfJQHdqFRQWs/FAd9KIEqCTiBgBZiR0AZCFuVKiRIxffN358TbK7qZ8BVABrdgMAAAAAAABQdiS7ATKsg1TIBkDlEDcAyErsACALcQNaV3V1dTj11FPDyy+/XOyitFnqmJZiGnOAPKeGilvzzzc1FEAlETcAyErsACALcQNaz6JFi8Jhhx0WbrrpptCuXbtwySWXFLtIbY46piVJdgMAAAAAAFDxFi5cGL7zne+E2267LXz/+98PP/nJT4pdpDZHHdPSJLsB8rAoV5W2Qs4HoHKIGwBkJXYAkIW4Aa3joIMOCnfeeWfo3r17qKqqCieeeGJe5w0bNiyst956rV6+tkAd09IkuwHysCi0S1vzzzc1FEAlETcAyErsACALcQNaZw3pCRMmpH/Pnj07XHnllXmf+//+3/+TiM2DOqY1ND8aAlSQ6ly7gjcAKoe4AUBWYgcAWYgb0PLi2tEPPfRQ6NmzZ1hllVXC3//+95DL5fLadt1112IXvyyo4+Vr0qRJYdy4cYvtf+WVV8KYMWNCWyGiAQAAAAAAUPG22mqr8PDDD4cVVlgh7LnnnuGJJ54odpHaHHW8/PzoRz8Kxx9/fLjsssvq9r300kup48A555wTpk+fHtoCyW6ADFNDFbIBUDnEDQCyEjsAyELcgNaz+eabh0ceeSR06tQpjUKm5anj5ePWW28NX/ziF8Opp54a7r777rRvt912C/Pnzw/3339/GDBgQGgLrNkNkIfq2IjIVRV0PgCVQ9wAICuxA4AsxA1oXZtsskkaAbvqqqsWuyhtljpufV27dg333ntv2G+//VLngmjhwoXhwQcfDNtss01oK3TfAgAAAKggY8eODZtttlno1q1b2nbYYYdwzz33LPWc2267LWywwQZp9M2mm25aNzIEAKCtkoRtfeq49XXp0iXcddddacr4Xr16pZH0bSnRHRnZDZCH6tAubYWcD0DlEDcAKOXY0b9//zB69Oiw3nrrhVwuF2644Ybw9a9/PTz77LNh4403Xuz4uI7iwQcfHEaNGhW+9rWvhZtuuinsv//+YdKkSWlEDgDLnzYHdf7972KXAChxnTt3TtOWt1WS3QB5WJRrl7ZCzgegcogbABQrdsyZM6fB/o4dO6atvjiNYX0XXXRRGu391FNPNZnsHjNmTPjqV78aTjvttHT/ggsuCA888EC44oorwrhx45pdZgCaT5sDAGqIaAB5qA5VBW8AVA5xA4BixY4BAwaE7t27121xNPbSLFq0KNx8881h3rx5aTrzpjz55JNhjz32aLBvr732SvsBKA5tDgCoYWQ3AAAAQBsxffr0tA53rcajumu98MILKbn92WefhZVXXjnccccdYaONNmry2BkzZoQ11lijwb54P+4HAAAoJslugDyYGgqALMQNAIoVO2Kiu36ye0kGDx4cnnvuuTB79uzw+9//PgwZMiQ8+uijS0x4A1BatDkAoIZkN0AeFoV2aSvkfAAqh7gBQKnHjg4dOoR11103/XvrrbcOTz/9dFqb+6qrrlrs2N69e4eZM2c22Bfvx/0AFIc2BwDUENEAAAAAKlx1dXWYP39+kz+L050/9NBDDfY98MADS1zjGwAAYHkxshsgD9W5qrQVcj4AlUPcAKCUY8fw4cPD3nvvHdZcc83w8ccfh5tuuilMmDAh3Hfffennhx12WOjXr18YNWpUuj9s2LCwyy6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      "text/plain": [
       "<Figure size 2000x400 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Q-values and Policy for Expected SARSA\n",
    "fig_es, axes_es = plt.subplots(1, len(action_space) + 1, figsize=(20, 4))\n",
    "\n",
    "plot_q_values_heatmap(es_q_table, rows, cols, action_space, fig_es, axes_es[:-1])\n",
    "axes_es[0].set_title(f\"Exp. SARSA Q-values: {action_space[0]}\") # Adjust titles\n",
    "axes_es[1].set_title(f\"Exp. SARSA Q-values: {action_space[1]}\")\n",
    "axes_es[2].set_title(f\"Exp. SARSA Q-values: {action_space[2]}\")\n",
    "axes_es[3].set_title(f\"Exp. SARSA Q-values: {action_space[3]}\")\n",
    "\n",
    "plot_policy_grid(es_q_table, rows, cols, terminal_states, axes_es[-1])\n",
    "axes_es[-1].set_title(\"Expected SARSA Learned Policy\")\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of Expected SARSA Q-Values and Policy**\n",
    "\n",
    "**Q-value Heatmaps (Left Four Plots)**\n",
    "- These heatmaps illustrate the learned **Q-values for each action** (up, down, left, right) across all grid states.\n",
    "- **Brighter colors (yellow/green) indicate higher Q-values**, meaning those actions are more favorable in certain states.\n",
    "- **The \"down\" and \"right\" actions have the highest Q-values** in most states, which aligns with an optimal strategy directing the agent toward the goal.\n",
    "- **Propagation of values is visible**, especially near the terminal states, where rewards influence neighboring states.\n",
    "\n",
    "**Learned Policy (Rightmost Plot)**\n",
    "- The **optimal policy derived from Q-values is visualized using arrows** pointing in the direction of the best action in each state.\n",
    "- **The policy primarily follows a right (→) and down (↓) movement pattern**, guiding the agent efficiently toward the bottom-right goal state.\n",
    "- **Up (↑) and left (←) actions appear in select states**, likely due to early exploration effects or the presence of alternative but suboptimal paths.\n",
    "- The **terminal states (‘T’) are correctly learned**, as indicated in red.\n",
    "\n",
    "**Overall Interpretation:**\n",
    "- The agent has successfully **learned a stable policy**, as seen in the **clear structure of Q-values and directional arrows**.\n",
    "- **Expected SARSA has effectively propagated rewards** from goal states back to earlier states, creating an optimal path.\n",
    "- **Compared to Q-learning**, which follows a more deterministic update rule, **Expected SARSA’s updates are influenced by the agent’s policy, leading to a potentially smoother learning process**.\n",
    "- **Minimal noise or instability is observed in the final Q-values**, suggesting **good convergence**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Q-Values and Optimal Policy\n",
    "Let's examine the optimal policy learned by SARSA in a tabular format, showing the Q-values for each action in each state and the corresponding best action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State      up         down       left       right      Optimal Action \n",
      "-----------------------------------------------------------------\n",
      "(0, 0)     0.00       0.00       0.00       0.00       Terminal       \n",
      "(0, 1)     0.34       0.17       1.00       0.20       left           \n",
      "(0, 2)     0.09       0.46       0.79       0.42       left           \n",
      "(0, 3)     0.21       6.67       0.02       0.88       down           \n",
      "(1, 0)     1.00       0.65       0.17       0.13       up             \n",
      "(1, 1)     0.83       0.25       0.15       0.53       up             \n",
      "(1, 2)     0.04       7.10       0.04       0.95       down           \n",
      "(1, 3)     0.16       8.63       0.91       1.93       down           \n",
      "(2, 0)     0.64       0.01       0.02       5.69       right          \n",
      "(2, 1)     0.18       0.74       0.60       7.32       right          \n",
      "(2, 2)     1.80       4.46       1.77       8.64       right          \n",
      "(2, 3)     5.42       10.00      3.52       5.88       down           \n",
      "(3, 0)     0.10       0.33       0.02       5.90       right          \n",
      "(3, 1)     1.91       1.11       0.00       8.34       right          \n",
      "(3, 2)     1.62       1.73       1.31       10.00      right          \n",
      "(3, 3)     0.00       0.00       0.00       0.00       Terminal       \n"
     ]
    }
   ],
   "source": [
    "# --- Tabular View ---\n",
    "# Create a list to store the Q-values and optimal actions for each state\n",
    "es_q_policy_data = []\n",
    "\n",
    "# Iterate over each row and column in the grid\n",
    "for r in range(rows):\n",
    "    for c in range(cols):\n",
    "        state = (r, c)  # Define the current state as a tuple (row, col)\n",
    "\n",
    "        # Check if the state exists in the Q-table\n",
    "        if state in es_q_table:\n",
    "            actions = es_q_table[state]  # Get the Q-values for all actions in the current state\n",
    "\n",
    "            # If the state has valid Q-values\n",
    "            if actions:\n",
    "                # Determine the best action (action with the highest Q-value)\n",
    "                # If the state is a terminal state, mark the optimal action as 'Terminal'\n",
    "                best_action = max(actions, key=actions.get) if state not in terminal_states else 'Terminal'\n",
    "\n",
    "                # Append the state, Q-values, and optimal action to the data list\n",
    "                es_q_policy_data.append({\n",
    "                    'State': state,\n",
    "                    'up': actions.get('up', 0.0),  # Q-value for 'up' action (default to 0.0 if not present)\n",
    "                    'down': actions.get('down', 0.0),  # Q-value for 'down' action\n",
    "                    'left': actions.get('left', 0.0),  # Q-value for 'left' action\n",
    "                    'right': actions.get('right', 0.0),  # Q-value for 'right' action\n",
    "                    'Optimal Action': best_action  # Best action for the current state\n",
    "                })\n",
    "            else:\n",
    "                # If the state has no valid Q-values, append default values\n",
    "                es_q_policy_data.append({\n",
    "                    'State': state,\n",
    "                    'up': 0.0,\n",
    "                    'down': 0.0,\n",
    "                    'left': 0.0,\n",
    "                    'right': 0.0,\n",
    "                    'Optimal Action': 'N/A'  # Mark as 'N/A' for no valid actions\n",
    "                })\n",
    "        else:\n",
    "            # If the state is not in the Q-table, append default values\n",
    "            es_q_policy_data.append({\n",
    "                'State': state,\n",
    "                'up': 0.0,\n",
    "                'down': 0.0,\n",
    "                'left': 0.0,\n",
    "                'right': 0.0,\n",
    "                'Optimal Action': 'N/A'  # Mark as 'N/A' for missing state\n",
    "            })\n",
    "\n",
    "# Sort the data by state for better readability\n",
    "es_q_policy_data.sort(key=lambda x: x['State'])\n",
    "\n",
    "# Define the header for the tabular output\n",
    "header = ['State', 'up', 'down', 'left', 'right', 'Optimal Action']\n",
    "\n",
    "# Print the header\n",
    "print(f\"{header[0]:<10} {header[1]:<10} {header[2]:<10} {header[3]:<10} {header[4]:<10} {header[5]:<15}\")\n",
    "print(\"-\" * 65)  # Print a separator line\n",
    "\n",
    "# Print each row of the Q-values and optimal actions\n",
    "for row_data in es_q_policy_data:\n",
    "    print(f\"{row_data['State']!s:<10} {row_data['up']:<10.2f} {row_data['down']:<10.2f} {row_data['left']:<10.2f} {row_data['right']:<10.2f} {row_data['Optimal Action']:<15}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of Tabular Expected SARSA Q-Values and Policy**  \n",
    "\n",
    "**Key Observations:**  \n",
    "\n",
    "**1. Q-Value Trends and Value Propagation:**  \n",
    "- **Higher Q-values are observed in states closer to the goal**, indicating the agent has effectively learned to estimate long-term rewards.  \n",
    "- For instance, **state (3,2) has a high Q-value for moving right**, as it directly leads to the terminal goal.  \n",
    "- The **Q-values propagate outward from the goal**, decreasing gradually as states become farther from it.\n",
    "\n",
    "**2. Optimal Action Selection and Policy Structure:**  \n",
    "- The **Optimal Action** column confirms that the agent follows a logical trajectory toward the goal.\n",
    "- **Right (→) and Down (↓) actions dominate** in most states, aligning with an efficient path to the goal.\n",
    "- **Occasional Up (↑) and Left (←) choices** appear where alternative paths were explored, reflecting the impact of **on-policy learning** (SARSA’s epsilon-greedy behavior).\n",
    "\n",
    "**3. Terminal States and Learning Behavior:**  \n",
    "- Terminal states, including the **goal state (3,3), are correctly identified**, preventing further action selection.\n",
    "- **Q-values in terminal states remain at zero**, as they do not contribute to future rewards.\n",
    "\n",
    "**4. Risk-Aware Behavior and Cliff Avoidance:**  \n",
    "- **States adjacent to dangerous areas (like cliffs or obstacles) show varied Q-values**, indicating the agent has learned to weigh the risks of different actions.\n",
    "- SARSA’s **on-policy learning** approach results in a more **cautious strategy**, avoiding dangerous states where rewards could be lost.\n",
    "- This risk-averse tendency is **a key difference from Q-learning**, which might have learned a more aggressive policy with riskier moves.\n",
    "\n",
    "**5. Alignment with Heatmap Visualizations:**  \n",
    "- The **tabular Q-values match the heatmap visualizations**, verifying that the policy structure remains consistent.\n",
    "- Minor differences in action selection **can be attributed to tie-breaking when multiple actions have similar Q-values**.\n",
    "\n",
    "**Comparison to Q-Learning:**\n",
    "- **SARSA’s on-policy updates** prioritize safety, avoiding risky moves near the cliff.\n",
    "- **Q-learning (off-policy)** may have resulted in a **more aggressive** policy, focusing solely on the highest expected return.\n",
    "- This difference is especially noticeable in **cliff-adjacent states**, where SARSA is more cautious."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing with Different Hyperparameters (Optional)\n",
    "\n",
    "Experiment with different values for the learning rate (α), discount factor (γ), and initial epsilon (ε₀) to observe their impact on Expected SARSA's learning speed and convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Expected SARSA hyperparameter experiments...\n",
      "  Training ES with alpha=0.1, gamma=0.9, epsilon_init=1.0\n",
      "  Training ES with alpha=0.1, gamma=0.99, epsilon_init=1.0\n",
      "  Training ES with alpha=0.5, gamma=0.9, epsilon_init=1.0\n",
      "  Training ES with alpha=0.5, gamma=0.99, epsilon_init=1.0\n",
      "Experiments finished.\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Run Expected SARSA Hyperparameter Experiments ---\n",
    "\n",
    "# Define hyperparameter ranges for experiments\n",
    "learning_rates_es_exp = [0.1, 0.5]  # Different learning rates (alpha) to test\n",
    "discount_factors_es_exp = [0.9, 0.99]  # Different discount factors (gamma) to test\n",
    "exploration_rates_es_exp = [1.0]  # Fixed initial epsilon for this comparison\n",
    "\n",
    "# List to store results of experiments\n",
    "es_results_exp = []\n",
    "\n",
    "# Print message indicating the start of experiments\n",
    "print(\"Running Expected SARSA hyperparameter experiments...\")\n",
    "\n",
    "# Iterate over all combinations of learning rates, discount factors, and exploration rates\n",
    "for alpha in learning_rates_es_exp:\n",
    "    for gamma in discount_factors_es_exp:\n",
    "        for initial_epsilon in exploration_rates_es_exp:\n",
    "            # Print the current hyperparameter combination being tested\n",
    "            print(f\"  Training ES with alpha={alpha}, gamma={gamma}, epsilon_init={initial_epsilon}\")\n",
    "            \n",
    "            # Run Expected SARSA with the current hyperparameter combination\n",
    "            q_table, rewards_per_episode, episode_lengths = run_expected_sarsa(\n",
    "                state_space, action_space, rewards, terminal_states, rows, cols, alpha, gamma,\n",
    "                initial_epsilon, min_epsilon_es, decay_rate_es, episodes_es, max_steps_es  # Use same min/decay as before\n",
    "            )\n",
    "            \n",
    "            # Store the results for this combination\n",
    "            es_results_exp.append({\n",
    "                'alpha': alpha,  # Learning rate\n",
    "                'gamma': gamma,  # Discount factor\n",
    "                'initial_epsilon': initial_epsilon,  # Initial exploration rate\n",
    "                'rewards_per_episode': rewards_per_episode,  # Rewards per episode\n",
    "                'episode_lengths': episode_lengths  # Episode lengths\n",
    "            })\n",
    "\n",
    "# Print message indicating the completion of experiments\n",
    "print(\"Experiments finished.\")\n",
    "\n",
    "# --- Visualization ---\n",
    "\n",
    "# Determine the number of subplots needed for visualization\n",
    "num_results_es = len(es_results_exp)  # Total number of experiments\n",
    "plot_rows_es = int(np.ceil(np.sqrt(num_results_es)))  # Number of rows in the grid of subplots\n",
    "plot_cols_es = int(np.ceil(num_results_es / plot_rows_es))  # Number of columns in the grid of subplots\n",
    "\n",
    "# Create a larger figure to visualize all hyperparameter combinations\n",
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "# Iterate over the results and plot the rewards for each experiment\n",
    "for i, result in enumerate(es_results_exp):\n",
    "    plt.subplot(plot_rows_es, plot_cols_es, i + 1)  # Create a subplot for the current experiment\n",
    "    plt.plot(result['rewards_per_episode'])  # Plot rewards per episode\n",
    "    plt.title(f\"Exp. SARSA: α={result['alpha']}, γ={result['gamma']}, ε₀={result['initial_epsilon']}\")  # Title with hyperparameters\n",
    "    plt.xlabel('Episode')  # Label for the x-axis\n",
    "    plt.ylabel('Total Reward')  # Label for the y-axis\n",
    "    plt.grid(True)  # Add grid for better readability\n",
    "    # Set y-axis limits based on the minimum and maximum rewards across all experiments\n",
    "    plt.ylim(\n",
    "        min(min(r['rewards_per_episode']) for r in es_results_exp) - 1,\n",
    "        max(max(r['rewards_per_episode']) for r in es_results_exp) + 1\n",
    "    )\n",
    "\n",
    "\n",
    "# Add a super title for the entire figure\n",
    "plt.suptitle(\"Expected SARSA Performance with Different Hyperparameters\", fontsize=16, y=1.02)\n",
    "\n",
    "# Adjust layout to prevent overlap and display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Expected SARSA to Different Environments (Cliff Walking)\n",
    "\n",
    "Now, apply Expected SARSA to the Cliff Walking environment and compare its behavior. We expect it might also learn a safer path like SARSA, potentially with smoother reward improvements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-use hyperparameters from SARSA cliff experiment for comparison\n",
    "alpha_cliff_es = 0.1\n",
    "gamma_cliff_es = 0.99 # Use the higher gamma\n",
    "initial_epsilon_cliff_es = 0.2 # Start with lower exploration for potentially faster safe path finding\n",
    "min_epsilon_cliff_es = 0.01\n",
    "decay_rate_cliff_es = 0.005\n",
    "episodes_cliff_es = 500\n",
    "max_steps_cliff_es = 200\n",
    "\n",
    "# Define environment parameters again\n",
    "cliff_rows, cliff_cols = 4, 12\n",
    "cliff_start_state = (3, 0)\n",
    "cliff_terminal_state = (3, 11)\n",
    "cliff_states = [(3, c) for c in range(1, 11)]\n",
    "cliff_action_space = ['up', 'down', 'left', 'right']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define rewards: -1 for normal steps, -100 for cliff, +10 for goal (implicit goal reward handled by update)\n",
    "# SARSA/Q-learning typically uses reward for *transitioning* into a state.\n",
    "# We can simulate the standard Cliff Walking rewards: -1 per step, -100 for falling.\n",
    "# Let's adapt the reward/transition logic slightly for this common setup.\n",
    "\n",
    "# Modified State Transition and Reward for standard Cliff Walking\n",
    "def cliff_state_transition_reward(\n",
    "    state: Tuple[int, int],\n",
    "    action: str,\n",
    "    rows: int,\n",
    "    cols: int,\n",
    "    cliff_states: List[Tuple[int, int]],\n",
    "    start_state: Tuple[int, int]\n",
    ") -> Tuple[Tuple[int, int], int]:\n",
    "    \"\"\"\n",
    "    Compute the next state and reward for Cliff Walking.\n",
    "    Reward is -1 for normal steps, -100 for falling into cliff.\n",
    "    Falling into cliff resets state to start_state.\n",
    "    \"\"\"\n",
    "    row, col = state\n",
    "    next_row, next_col = row, col\n",
    "\n",
    "    # Calculate potential next position\n",
    "    if action == 'up' and row > 0:\n",
    "        next_row -= 1\n",
    "    elif action == 'down' and row < rows - 1:\n",
    "        next_row += 1\n",
    "    elif action == 'left' and col > 0:\n",
    "        next_col -= 1\n",
    "    elif action == 'right' and col < cols - 1:\n",
    "        next_col += 1\n",
    "\n",
    "    next_state = (next_row, next_col)\n",
    "\n",
    "    # Determine reward\n",
    "    if next_state in cliff_states:\n",
    "        reward = -100\n",
    "        next_state = start_state # Reset to start if falls into cliff\n",
    "    elif next_state == cliff_terminal_state:\n",
    "        reward = 0 # Standard setup often gives 0 for reaching goal, -1 transition cost handled step reward\n",
    "                   # Alternative: give +10 or other positive reward here if desired. Let's stick to -1 step cost.\n",
    "        reward = -1 # Apply step cost even for reaching goal for consistency? No, goal transition is special.\n",
    "                    # Let's use the common -1 step cost structure. Goal state itself gives no reward/penalty on arrival.\n",
    "        reward = -1\n",
    "    else:\n",
    "        reward = -1 # Standard step cost\n",
    "\n",
    "    return next_state, reward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a specific Expected SARSA runner for the Cliff Walking environment\n",
    "def run_expected_sarsa_cliff_episode(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]],  # Q-table mapping states to actions and their Q-values\n",
    "    action_space: List[str],  # List of possible actions\n",
    "    terminal_state: Tuple[int, int],  # Terminal state in the environment\n",
    "    cliff_states: List[Tuple[int, int]],  # List of cliff states in the environment\n",
    "    start_state: Tuple[int, int],  # Starting state for the episode\n",
    "    rows: int,  # Number of rows in the grid\n",
    "    cols: int,  # Number of columns in the grid\n",
    "    alpha: float,  # Learning rate (0 < alpha <= 1)\n",
    "    gamma: float,  # Discount factor (0 <= gamma <= 1)\n",
    "    epsilon: float,  # Exploration rate (0 <= epsilon <= 1)\n",
    "    max_steps: int  # Maximum number of steps allowed per episode\n",
    ") -> Tuple[int, int]:\n",
    "    \"\"\"\n",
    "    Runs a single episode of Expected SARSA for the Cliff Walking environment.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table: The Q-table storing Q-values for all state-action pairs.\n",
    "    - action_space: List of all possible actions.\n",
    "    - terminal_state: The terminal state in the environment.\n",
    "    - cliff_states: List of cliff states in the environment.\n",
    "    - start_state: The starting state for the episode.\n",
    "    - rows: Number of rows in the grid.\n",
    "    - cols: Number of columns in the grid.\n",
    "    - alpha: Learning rate (0 < alpha <= 1).\n",
    "    - gamma: Discount factor (0 <= gamma <= 1).\n",
    "    - epsilon: Exploration rate (0 <= epsilon <= 1).\n",
    "    - max_steps: Maximum number of steps allowed per episode.\n",
    "\n",
    "    Returns:\n",
    "    - Tuple containing:\n",
    "        - total_reward: Total reward accumulated during the episode.\n",
    "        - steps: Number of steps taken in the episode.\n",
    "    \"\"\"\n",
    "    # Initialize the starting state\n",
    "    state: Tuple[int, int] = start_state\n",
    "    total_reward: int = 0  # Accumulate total reward for the episode\n",
    "    steps: int = 0  # Track the number of steps taken in the episode\n",
    "\n",
    "    # Loop for a maximum number of steps\n",
    "    for _ in range(max_steps):\n",
    "        # Choose an action using the epsilon-greedy policy\n",
    "        action: str = epsilon_greedy_policy(state, q_table, action_space, epsilon)\n",
    "\n",
    "        # Take the chosen action and observe the next state and reward\n",
    "        next_state, reward = cliff_state_transition_reward(\n",
    "            state, action, rows, cols, cliff_states, start_state\n",
    "        )\n",
    "        total_reward += reward  # Update the total reward\n",
    "\n",
    "        # Update the Q-value for the current state-action pair using the Expected SARSA rule\n",
    "        # Pass the terminal state as a list to the update function\n",
    "        update_expected_sarsa_value(\n",
    "            q_table, state, action, reward, next_state, alpha, gamma, epsilon, action_space, [terminal_state]\n",
    "        )\n",
    "\n",
    "        # Move to the next state\n",
    "        state = next_state\n",
    "        steps += 1  # Increment the step counter\n",
    "\n",
    "        # Terminate the episode if the terminal state is reached\n",
    "        if state == terminal_state:\n",
    "            break\n",
    "\n",
    "    return total_reward, steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_expected_sarsa_cliff(\n",
    "    action_space: List[str],  # List of possible actions (e.g., ['up', 'down', 'left', 'right'])\n",
    "    terminal_state: Tuple[int, int],  # The terminal state in the environment\n",
    "    cliff_states: List[Tuple[int, int]],  # List of cliff states in the environment\n",
    "    start_state: Tuple[int, int],  # The starting state for each episode\n",
    "    rows: int,  # Number of rows in the grid\n",
    "    cols: int,  # Number of columns in the grid\n",
    "    alpha: float,  # Learning rate (0 < alpha <= 1)\n",
    "    gamma: float,  # Discount factor (0 <= gamma <= 1)\n",
    "    initial_epsilon: float,  # Initial exploration rate (0 <= epsilon <= 1)\n",
    "    min_epsilon: float,  # Minimum exploration rate\n",
    "    decay_rate: float,  # Rate at which epsilon decays\n",
    "    episodes: int,  # Number of episodes to train\n",
    "    max_steps: int  # Maximum number of steps per episode\n",
    ") -> Tuple[Dict[Tuple[int, int], Dict[str, float]], List[int], List[int]]:\n",
    "    \"\"\"\n",
    "    Executes the Expected SARSA algorithm for the Cliff Walking environment.\n",
    "\n",
    "    Parameters:\n",
    "    - action_space (List[str]): List of all possible actions.\n",
    "    - terminal_state (Tuple[int, int]): The terminal state in the environment.\n",
    "    - cliff_states (List[Tuple[int, int]]): List of cliff states in the environment.\n",
    "    - start_state (Tuple[int, int]): The starting state for each episode.\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "    - alpha (float): Learning rate (0 < alpha <= 1).\n",
    "    - gamma (float): Discount factor (0 <= gamma <= 1).\n",
    "    - initial_epsilon (float): Initial exploration rate (0 <= epsilon <= 1).\n",
    "    - min_epsilon (float): Minimum exploration rate.\n",
    "    - decay_rate (float): Rate at which epsilon decays.\n",
    "    - episodes (int): Number of episodes to train.\n",
    "    - max_steps (int): Maximum number of steps per episode.\n",
    "\n",
    "    Returns:\n",
    "    - Tuple containing:\n",
    "        - q_table (Dict[Tuple[int, int], Dict[str, float]]): The Q-table storing Q-values for all state-action pairs.\n",
    "        - rewards_per_episode (List[int]): List of total rewards accumulated in each episode.\n",
    "        - episode_lengths (List[int]): List of the number of steps taken in each episode.\n",
    "    \"\"\"\n",
    "    # Generate the state space as all possible (row, col) pairs\n",
    "    state_space: List[Tuple[int, int]] = [(r, c) for r in range(rows) for c in range(cols)]\n",
    "\n",
    "    # Initialize the Q-table with zeros for all state-action pairs\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]] = initialize_q_table_nested(state_space, action_space)\n",
    "\n",
    "    # Lists to store rewards and episode lengths for each episode\n",
    "    rewards_per_episode: List[int] = []\n",
    "    episode_lengths: List[int] = []\n",
    "\n",
    "    # Loop through each episode\n",
    "    for episode in range(episodes):\n",
    "        # Dynamically adjust epsilon for the current episode\n",
    "        epsilon: float = adjust_epsilon(initial_epsilon, min_epsilon, decay_rate, episode)\n",
    "\n",
    "        # Run a single episode of Expected SARSA for the Cliff Walking environment\n",
    "        total_reward, steps = run_expected_sarsa_cliff_episode(\n",
    "            q_table, action_space, terminal_state, cliff_states, start_state,\n",
    "            rows, cols, alpha, gamma, epsilon, max_steps\n",
    "        )\n",
    "\n",
    "        # Store the total reward and episode length\n",
    "        rewards_per_episode.append(total_reward)\n",
    "        episode_lengths.append(steps)\n",
    "\n",
    "    # Return the Q-table, rewards per episode, and episode lengths\n",
    "    return q_table, rewards_per_episode, episode_lengths\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run Expected SARSA for Cliff Walking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Expected SARSA on Cliff Walking environment...\n",
      "Expected SARSA training on Cliff Walking finished.\n"
     ]
    }
   ],
   "source": [
    "# Re-use hyperparameters from SARSA cliff experiment for comparison\n",
    "alpha_cliff_es = 0.1  # Learning rate: controls the step size for Q-value updates\n",
    "gamma_cliff_es = 0.99  # Discount factor: determines the importance of future rewards\n",
    "initial_epsilon_cliff_es = 0.2  # Initial exploration rate: probability of choosing a random action\n",
    "min_epsilon_cliff_es = 0.01  # Minimum exploration rate: lower bound for epsilon\n",
    "decay_rate_cliff_es = 0.005  # Epsilon decay rate: controls how quickly epsilon decreases\n",
    "episodes_cliff_es = 500  # Number of episodes to train the agent\n",
    "max_steps_cliff_es = 200  # Maximum number of steps allowed per episode\n",
    "\n",
    "# Print a message indicating the start of training\n",
    "print(\"Running Expected SARSA on Cliff Walking environment...\")\n",
    "\n",
    "# Define environment parameters for the Cliff Walking environment\n",
    "cliff_rows, cliff_cols = 4, 12  # Grid dimensions (4 rows x 12 columns)\n",
    "cliff_start_state = (3, 0)  # Starting state for each episode\n",
    "cliff_terminal_state = (3, 11)  # Terminal state (goal state)\n",
    "cliff_states = [(3, c) for c in range(1, 11)]  # Cliff states (dangerous states to avoid)\n",
    "cliff_action_space = ['up', 'down', 'left', 'right']  # Possible actions in the environment\n",
    "\n",
    "# Run the Expected SARSA training on the Cliff Walking environment\n",
    "cliff_q_table_es, cliff_rewards_es, cliff_lengths_es = run_expected_sarsa_cliff(\n",
    "    cliff_action_space,  # List of possible actions\n",
    "    cliff_terminal_state,  # Terminal state\n",
    "    cliff_states,  # List of cliff states\n",
    "    cliff_start_state,  # Starting state for each episode\n",
    "    cliff_rows,  # Number of rows in the grid\n",
    "    cliff_cols,  # Number of columns in the grid\n",
    "    alpha_cliff_es,  # Learning rate\n",
    "    gamma_cliff_es,  # Discount factor\n",
    "    initial_epsilon_cliff_es,  # Initial exploration rate\n",
    "    min_epsilon_cliff_es,  # Minimum exploration rate\n",
    "    decay_rate_cliff_es,  # Epsilon decay rate\n",
    "    episodes_cliff_es,  # Number of episodes to train\n",
    "    max_steps_cliff_es  # Maximum number of steps per episode\n",
    ")\n",
    "\n",
    "# Print a message indicating the completion of training\n",
    "print(\"Expected SARSA training on Cliff Walking finished.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot the rewards, we can use the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot rewards for the Cliff Walking environment\n",
    "def plot_rewards(rewards_per_episode: List[int], ax: plt.Axes = None) -> plt.Axes:\n",
    "    \"\"\"\n",
    "    Plot the total rewards accumulated over episodes.\n",
    "\n",
    "    Parameters:\n",
    "    - rewards_per_episode (List[int]): List of total rewards per episode.\n",
    "    - ax (plt.Axes, optional): Matplotlib axis to plot on. If None, a new figure and axis are created.\n",
    "\n",
    "    Returns:\n",
    "    - plt.Axes: The Matplotlib axis containing the plot.\n",
    "    \"\"\"\n",
    "    # If no axis is provided, create a new figure and axis\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(8, 6))\n",
    "    \n",
    "    # Plot the rewards over episodes\n",
    "    ax.plot(rewards_per_episode)\n",
    "    ax.set_xlabel('Episode')  # Label for the x-axis\n",
    "    ax.set_ylabel('Total Reward')  # Label for the y-axis\n",
    "    ax.set_title('Rewards Over Episodes')  # Title of the plot\n",
    "    \n",
    "    # Return the axis for further customization if needed\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the results, we can plot the rewards and episode lengths similar to before. Let's also visualize the learned policy for the Cliff Walking environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Visualization for Cliff Walking (Expected SARSA) ---\n",
    "fig_cliff_es, axs_cliff_es = plt.subplots(2, 2, figsize=(18, 8))\n",
    "\n",
    "# Rewards\n",
    "plot_rewards(cliff_rewards_es, ax=axs_cliff_es[0, 0])\n",
    "axs_cliff_es[0, 0].set_title(\"Exp. SARSA: Cliff Walking Rewards\")\n",
    "axs_cliff_es[0, 0].grid(True)\n",
    "\n",
    "# Episode Lengths\n",
    "axs_cliff_es[0, 1].plot(cliff_lengths_es)\n",
    "axs_cliff_es[0, 1].set_xlabel('Episode')\n",
    "axs_cliff_es[0, 1].set_ylabel('Episode Length')\n",
    "axs_cliff_es[0, 1].set_title('Exp. SARSA: Cliff Walking Episode Lengths')\n",
    "axs_cliff_es[0, 1].grid(True)\n",
    "\n",
    "# Plot Max Q-Values (Heatmap) - Need a function that takes cliff env params\n",
    "def plot_q_values_cliff(q_table, rows, cols, ax):\n",
    "    q_values = np.zeros((rows, cols))\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            state = (r, c)\n",
    "            if state in q_table and q_table[state]:\n",
    "                q_values[r, c] = max(q_table[state].values())\n",
    "            else:\n",
    "                q_values[r,c] = -np.inf # Mark unvisited/terminal\n",
    "\n",
    "    # Mask cliff states for better visualization\n",
    "    q_values_masked = np.ma.masked_where(q_values <= -100, q_values) # Hide extreme negatives from cliff\n",
    "\n",
    "    im = ax.imshow(q_values_masked, cmap='viridis')\n",
    "    plt.colorbar(im, ax=ax, label='Max Q-Value')\n",
    "    ax.set_title('Expected SARSA: Max Q-Values (Cliff)')\n",
    "    ax.set_xticks(np.arange(cols))\n",
    "    ax.set_yticks(np.arange(rows))\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ax.grid(which='major', color='w', linestyle='-', linewidth=1)\n",
    "\n",
    "# Max Q-Values\n",
    "plot_q_values_cliff(cliff_q_table_es, cliff_rows, cliff_cols, ax=axs_cliff_es[1, 0])\n",
    "axs_cliff_es[1, 0].set_title('Exp. SARSA: Max Q-Values (Cliff)')\n",
    "\n",
    "# Policy\n",
    "plot_policy_grid(cliff_q_table_es, cliff_rows, cliff_cols, [cliff_terminal_state], ax=axs_cliff_es[1, 1])\n",
    "axs_cliff_es[1, 1].set_title(\"Exp. SARSA: Learned Policy (Cliff)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
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   "cell_type": "markdown",
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    "**Updated Analysis of Expected SARSA on Cliff Walking:**\n",
    "\n",
    "**1. Rewards & Episode Lengths (Top Row):**\n",
    "- The **total rewards graph** shows an initial sharp drop, reflecting early episodes where the agent frequently falls into the cliff, leading to large negative rewards.\n",
    "- Over time, the **rewards improve and stabilize** as the agent learns to avoid the cliff and follow a safer path.\n",
    "- The **episode lengths decrease consistently**, indicating that the agent is learning to reach the goal more efficiently with fewer steps.\n",
    "- The **smoother convergence pattern** suggests that Expected SARSA's expected value updates provide more stable learning compared to standard SARSA.\n",
    "\n",
    "**2. Max Q-Values & Policy (Bottom Row):**\n",
    "- The **Q-value heatmap** reinforces the learning process, with lower values (darker regions) near the cliff, emphasizing the high penalty of falling off.\n",
    "- **Higher Q-values (lighter areas) are concentrated along the safer path**, indicating the agent's preferred trajectory.\n",
    "- The **learned policy grid** reveals a strategy where the agent initially moves **upward** to avoid immediate danger, then proceeds **right along the upper row** before moving **down to the goal**.\n",
    "- This conservative route ensures that the agent minimizes the risk of stepping into the cliff zone, highlighting **Expected SARSA’s risk-aware nature**.\n",
    "\n",
    "**Conclusion for Cliff Walking (Expected SARSA):**\n",
    "Expected SARSA successfully **learns a stable and cautious policy**, favoring safer paths over riskier but shorter routes.  \n",
    "Compared to standard SARSA:\n",
    "- It converges **more smoothly** due to its expected value updates, reducing variability.\n",
    "- The final policy remains **on-policy and risk-averse**, avoiding the cliff while maintaining efficiency.\n",
    "- **Expected SARSA proves effective in environments where stability and risk minimization are critical.**"
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    "## Common Challenges and Solutions\n",
    "\n",
    "(Reused from SARSA, as challenges are similar)\n",
    "\n",
    "**Challenge: Slow learning or getting stuck**\n",
    "*   **Solution**: Tune hyperparameters (α, γ, ε decay). Increase exploration initially or use more sophisticated exploration. Function approximation for larger state spaces.\n",
    "\n",
    "**Challenge: Balancing exploration and exploitation**\n",
    "*   **Solution**: Use a well-tuned epsilon decay schedule.\n",
    "\n",
    "**Challenge: Choosing appropriate hyperparameters**\n",
    "*   **Solution**: Experimentation. Start with common values (α≈0.1, γ≈0.9-0.99, ε decaying from 1.0 to 0.1/0.01).\n",
    "\n",
    "**Challenge: Computational cost of expectation**\n",
    "*   **Solution**: In tabular cases with few actions, the summation is cheap. For large/continuous action spaces, calculating the exact expectation is infeasible, requiring different techniques (e.g., sampling, function approximation for the policy).\n",
    "\n",
    "## Expected SARSA vs. Other Reinforcement Learning Algorithms\n",
    "\n",
    "### Advantages of Expected SARSA\n",
    "-   **On-Policy:** Learns the value of the policy being followed.\n",
    "-   **Lower Variance:** Typically has lower variance updates than SARSA because it averages over possible next actions instead of relying on a single sample. This can lead to more stable and sometimes faster learning.\n",
    "-   **Often More Stable/Conservative:** Inherits SARSA's tendency to learn safer policies in risky environments.\n",
    "-   Guaranteed Convergence under standard conditions.\n",
    "\n",
    "### Limitations of Expected SARSA\n",
    "-   **Can be Slower than Q-Learning:** Still learns based on the current policy, which might explore suboptimal actions, potentially slowing convergence to the truly optimal policy compared to off-policy Q-learning.\n",
    "-   **Computational Cost:** Requires iterating through all actions in the next state to compute the expectation, which can be slightly more computationally expensive per update than SARSA or Q-Learning if the action space is large (though negligible in small, discrete action spaces). Not directly applicable to continuous action spaces without modification.\n",
    "-   **State Space Limitations:** Tabular version struggles with large state spaces.\n",
    "\n",
    "### Related Algorithms\n",
    "-   **SARSA**: The base on-policy TD algorithm from which Expected SARSA is derived.\n",
    "-   **Q-Learning**: Off-policy TD algorithm, learns the optimal policy directly.\n",
    "-   **SARSA(λ) / Expected SARSA(λ)**: Versions using eligibility traces for faster credit assignment.\n",
    "-   **Actor-Critic Methods**: Learn both a policy and a value function.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Expected SARSA is an effective on-policy temporal difference control algorithm that improves upon standard SARSA by reducing update variance. It achieves this by using the *expected* Q-value of the next state, averaged over all actions according to the current policy, rather than relying on the single next action sampled.\n",
    "\n",
    "This often leads to smoother and potentially faster convergence while retaining SARSA's on-policy characteristics, making it well-suited for evaluating the current policy and learning safer behaviors in risky environments like Cliff Walking. While slightly more computationally intensive per step than SARSA in the tabular case, its improved stability makes it a strong alternative on-policy learning method."
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